diff --git a/kaggle/Iris_Species/1st-model.ipynb b/kaggle/Iris_Species/1st-model.ipynb new file mode 100644 index 0000000..7a4553f --- /dev/null +++ b/kaggle/Iris_Species/1st-model.ipynb @@ -0,0 +1,450 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# কোডে প্রথম মডেল এবং প্রেডিকশন " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "এই পুরো জুপিটার স্ক্রিপ্টটা পাওয়া যাবে এই লিংকে \n", + "https://github.com/raqueeb/ml-python/blob/master/1st-model.ipynb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "এস্টিমেটরের কাজের ধাপের পুরো কোড এখানে। না বুঝলে আবার ফিরে যান \"এস্টিমেটরের কাজের ধাপ\" চ্যাপ্টারে। " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "মনে রাখুন এই ধাপগুলো, দরকার হবে সবসময় " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "প্রথমে কিছু লাইব্রেরি ইমপোর্ট করে নেই " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ডাটাসেটগুলো ইমপোর্ট করে নিয়ে আসি " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn import datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "আইরিস ডাটাসেট লোড করে নেই " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "iris = datasets.load_iris()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ফিচার ম্যাট্রিক্স স্টোর করছি বড় \"X\"এ, রেসপন্স ভেক্টর রাখছি \"y\" তে " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X = iris.data\n", + "y = iris.target" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ক্লাসিফায়ার ইমপোর্ট করে নিয়ে আসছি " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "আমাদের নেইবার সংখ্যা ১" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "knn = KNeighborsClassifier(n_neighbors=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "প্রথম মডেল তৈরি " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", + " metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n", + " weights='uniform')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn.fit(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "প্রথম প্রেডিকশন, আমাদের ডাটাসেটের বাইরের ডাটা দিয়ে " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn.predict([[3, 5, 4, 2]])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted target name: ['virginica']\n" + ] + } + ], + "source": [ + "print(\"Predicted target name:\",\n", + " iris['target_names'][knn.predict([[3, 5, 4, 2]])])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "অথবা আমরা এভাবে করতে পারি, আপনার মতো করে তৈরি করুন ইচ্ছেমতো " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_new.shape: (1, 4)\n" + ] + } + ], + "source": [ + "X_new = np.array([[3, 5, 4, 2]])\n", + "print(\"X_new.shape:\", X_new.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "mypredict = knn.predict(X_new)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prediction: [2]\n", + "Predicted target name: ['virginica']\n" + ] + } + ], + "source": [ + "print(\"Prediction:\", mypredict)\n", + "print(\"Predicted target name:\",\n", + " iris['target_names'][mypredict])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ধাপগুলো নিয়ে ধারণা পরিষ্কার তো? এখন যদি আমরা দুটো \"আউট অফ স্যাম্পল\" ডেটা নিয়ে কাজ করতাম, তাহলে কি করতাম আমরা?" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 1])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_new = [[3, 5, 4, 2], [5, 4, 3, 2]]\n", + "knn.predict(X_new)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "এখানে আমরা ব্যবহার করেছি \"কে নিয়ারেস্ট নেইবার্স\" ক্লাসিফায়ার। আচ্ছা, আমাদের যদি নেইবার ৩ হয়? তাহলে আগের সিস্টেমে পাল্টে দিলাম n_neighbors=3" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "knn = KNeighborsClassifier(n_neighbors=3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "মডেলে ফিট করি ডাটা " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", + " metric_params=None, n_jobs=1, n_neighbors=3, p=2,\n", + " weights='uniform')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn.fit(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "প্রেডিক্ট করি আগের ভ্যালুগুলোকে " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 1])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn.predict(X_new)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "দেখেছেন কী অবস্থা? পাল্টে গেছে প্রেডিকশন ভ্যালু। নিশ্চয়ই ক্লাসিফায়ারের কোন ভ্যালুতে মডেল ভালো কাজ করবে সেটা জানলে ব্যাপারটা আরো ভালো হতো। সেটা জানতেই তো এতো গল্প। " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "কেমন হয় অন্য ক্লাসিফায়ার দিয়ে দেখলে? \"লিনিয়ার রিগ্রেশন\" কাজ করে কন্টিনিউয়াস ভাল্যুর (যেমন, আমাদের বয়স বা বেতন) ওপর। সে হিসেবে লজিস্টিক (হ্যাঁ অথবা না, তিন ক্যাটেগরির ফুল) রিগ্রেশন ব্যবহার করা যেতে পারে এখানে। আগের মতোই একই জিনিস করবো আমরা। মডেল হিসেবে ব্যবহার করবো LogisticRegressionকে। সেটা ইমপোর্ট হবে sklearn.linear_model মডিউল থেকে। LogisticRegression() ক্লাসকে পাঠিয়ে দিচ্ছি lr অবজেক্ট। আপনার ঈচ্ছেমতো নাম দিন এই অবজেক্ট হিসেবে। " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 0])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "lr = LogisticRegression()\n", + "lr.fit(X, y)\n", + "lr.predict(X_new)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "দেখেছেন কী অবস্থা? পাল্টে গেছে প্রেডিকশন। এর মানে হচ্ছে আমাদের ব্যবহৃত ক্লাসিফায়ারগুলোর কাজের মধ্যে অনেক ফারাক আছে। সেকারণে আউটকামও ভিন্ন। কোন কাজে কোন ক্লাসিফায়ার ভালো, সেটার ধারণায় আসবে আস্তে আস্তে।" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/kaggle/Iris_Species/cross-validation.ipynb b/kaggle/Iris_Species/cross-validation.ipynb new file mode 100644 index 0000000..7c17dc5 --- /dev/null +++ b/kaggle/Iris_Species/cross-validation.ipynb @@ -0,0 +1,368 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ক্রস ভ্যালিডেশনের প্যারামিটারের টিউনিং, মডেল সিলেকশন \n", + "\n", + "জুপিটার নোটবুকের লিংক https://github.com/raqueeb/ml-python/blob/master/cross-validation.ipynb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### অ্যাক্যুরেসি বাড়ানোর জন্য \"কে ফোল্ড\" ক্রস ভ্যালিডেশন\n", + "\n", + "মডেল ইভ্যালুয়েশনে \"ট্রেইন/টেস্ট স্প্লিট\" দেখেছি আগের চ্যাপ্টারে। এখানে এই স্প্লিটে বেশি 'আউট অফ স্যাম্পল' ডেটার জন্য 'ভ্যারিয়েন্স' এস্টিমেট অনেক বেশি হতে পারে। কারণ কোন অবজারভেশনগুলো টেস্ট সেটে যাবে সেটা অনেক সময় আমাদের হাতে থাকে না। আর সেকারণে সেটার আউটকাম টেস্টিং অ্যাক্যুরেসিতে পড়তে পারে। আর আপনি যা করছেন সেটা একবার করছেন। একটা ছোট ডেটাসেটের আলাদা একটা টেস্টসেট তৈরি করতে গিয়ে ট্রেনিং ডেটাসেট কমে যায়। \n", + "\n", + "সেকারণে আমরা এখন চেষ্টা করবো নতুন একটা কনসেপ্ট, \"কে ফোল্ড\" ক্রস ভ্যালিডেশন। \"কে ফোল্ড\" অর্থ হচ্ছে ডেটাসেটকে কতোবার আমরা \"কে\" সংখ্যক একই ভাগে ভাগ করবো। " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### শুরুতেই লোড করে নেই আগের ডেটাসেট এবং দরকারি মডিউল " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.datasets import load_iris\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn import metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# লোড করে নেই আইরিস ডেটাসেট \n", + "iris = load_iris() \n", + "\n", + "# তৈরি করে নেই X (ফীচার) এবং y (রেসপন্স)\n", + "X = iris.data\n", + "y = iris.target" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### \"কে ফোল্ড\" ক্রস ভ্যালিডেশন করার কিছু স্টেপস " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "১. শুরুতেই আমাদের নতুন এই টুল পুরো ডেটাসেটকে ভাগ করবে সমান সাইজের পার্টিশনে। এই পার্টিশনগুলো হচ্ছে ফোল্ড, কয়টা ফোল্ড করতে চাই আমরা?\n", + "\n", + "২. নিচের ছবিটা দেখুন। পুরো ডেটাকে পাঁচ ভাগে ভাগ করেছে এই টুল। এখানে সবচেয়ে ডানের পার্টিশন মানে ফোল্ডকে সে ডিক্লেয়ার করেছে টেস্ট সেট হিসেবে। বাকি চার ভাগ ট্রেনিং সেট। ট্রেনিং সেট সবসময় টেস্ট সেট থেকে বড় রাখতে হয়। \n", + "\n", + "৩. এরপর সেটা বের করবে \"টেস্টিং অ্যাক্যুরেসি\"। সাধারণ সিস্টেমে যেভাবে আগে কাজ করেছি সেভাবে। \n", + "\n", + "৪. ২য় এবং ৩য় স্টেপগুলো বার বার করতে থাকবো যতক্ষণ পর্যন্ত একেকটা ফোল্ডকে একেকবার টেস্ট সেট হিসেবে ব্যবহার করে শেষ করে ফেলবো। এটা যদি \"৫ ফোল্ড\" ক্রস ভ্যালিডেশন হয়, এর মানে হচ্ছে পাঁচবার আলাদা করে টেস্ট সেট হিসেবে ধরে ট্রেনিং করে সেটার অ্যাক্যুরেসি সে মনে রাখবে। এগুলো আমরা খালি চোখে দেখবো না, এর সব কাজ হয়ে যাবে মডিউলের ভেতরে। \n", + "\n", + "৫. এই সবগুলোর \"টেস্টিং অ্যাক্যুরেসি\"কে গড় করে সেটাকে আমরা \"আউট অফ স্যাম্পল\" এর এস্টিমেট ধারণা করতে পারি। " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### \"৫ ফোল্ড\" ক্রস ভ্যালিডেশন এর একটা ছবি \n", + "\n", + "![5-fold cross-validation](assets/cv.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration Training set observations Testing set observations\n", + " 1 [ 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24] [0 1 2 3 4] \n", + " 2 [ 0 1 2 3 4 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24] [5 6 7 8 9] \n", + " 3 [ 0 1 2 3 4 5 6 7 8 9 15 16 17 18 19 20 21 22 23 24] [10 11 12 13 14] \n", + " 4 [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 20 21 22 23 24] [15 16 17 18 19] \n", + " 5 [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19] [20 21 22 23 24] \n" + ] + } + ], + "source": [ + "# এটা আমাদের কাজে লাগবেনা, তবে ধারণা নেবার জন্য এখানে দেয়া \n", + "# আমরা ২৫টা রেকর্ড নিয়ে সেটাকে ৫ ফোল্ডে ভাগ করে একটা সিমুলেশন দেখাই এখানে \n", + "\n", + "from sklearn.model_selection import KFold\n", + "kf = KFold(n_splits=5, shuffle=False).split(range(25))\n", + "\n", + "# প্রতিটা ট্রেনিং এবং টেস্ট সেট এর কনটেন্ট দেখি একটা একটা করে, সংখ্যাগুলো ডেটাসেটের একেকটা রেকর্ড \n", + "print('{} {:^61} {}'.format('Iteration', 'Training set observations', 'Testing set observations'))\n", + "for iteration, data in enumerate(kf, start=1):\n", + " print('{:^9} {} {:^25}'.format(iteration, data[0], str(data[1])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "আপনাদের বোঝানোর জন্য ছোট একটা পঁচিশটা অবজারভেশনের ডেটাসেট দেখানো হচ্ছে এখানে। এর সাথে আইরিস ডেটাসেটের কোন সম্পর্ক নেই। এর অবজারভেশনগুলো হচ্ছে ০ থেকে ২৪ পর্যন্ত।\n", + "\n", + "যেহেতু এটা একটা ৫ ফোল্ড ক্রস ভালিডেশন, সে কারণে এটার কিন্তু পাঁচটা ‘আইটারেশন’। একই জিনিস পাঁচবার আলাদা আলাদা করে চলবে।\n", + "\n", + "প্রতিটা ‘আইটারেশনে’, আমাদের এক একটা রেকর্ড - হয় ট্রেনিং সেটে অথবা টেস্টিং সেটে থাকবে, কিন্তু দু'জায়গায় একসময়ে থাকবে না। \n", + "\n", + "আবার টেস্টিং সেটে প্রতিটা অবজারভেশন একবারই আসবে, এর বেশি নয়।" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ক্রস ভ্যালিডেশনের কিছু বেস্ট প্রাক্টিসেস " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "১. আমাদের \"কে\" ফোল্ড অংশে \"কে\" এর সংখ্যা ইচ্ছেমতো হতে পারে তবে আমাদের রেকমেন্ডেশন হচ্ছে **K=10**।\n", + "\n", + "২. ক্লাসিফিকেশন সমস্যায় সেখানে আমরা \"স্ট্র্যাটিফাইড স্যাম্পলিং\" ব্যবহার করবো ফোল্ড তৈরি করার জন্য। প্রতিটা রেসপন্স ক্লাস কিন্তু একই অনুপাতে আনতে হবে প্রতিটা \"কে\" ফোল্ডে। এটা একটা সমস্যা। তবে সাইকিট লার্নের `cross_val_score` ফাংশন জিনিসটা করে দেয় এমনিতেই। আর সেকারণে সাইকিট লার্নের এতো নাম ডাক। " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### অ্যাক্যুরেসি বাড়াতে আইরিস ডেটাসেটের জন্য প্যারামিটার টিউনিং " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "অনেক গল্প শুনি আমরা \"হাইপার-প্যারামিটার\" টিউনিং নিয়ে। আইরিস ডেটাসেট নিয়ে \"কে নিয়ারেস্ট নেইবার\"এর সবচেয়ে ভালো টিউনিং প্যারামিটার কি হতে পারে? " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import cross_val_score" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 1. 0.93333333 1. 1. 0.86666667 0.93333333\n", + " 0.93333333 1. 1. 1. ]\n" + ] + } + ], + "source": [ + "# \"কে নিয়ারেস্ট নেইবার\" K=5, n_neighbors হচ্ছে প্যারামিটার + \"কে ফোল্ড\" ক্রস ভ্যালিডেশন এর cv=10\n", + "knn = KNeighborsClassifier(n_neighbors=5)\n", + "scores = cross_val_score(knn, X, y, cv=10, scoring='accuracy')\n", + "print(scores)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.966666666667\n" + ] + } + ], + "source": [ + "# গড় করি সবগুলোর \"আউট অফ স্যাম্পল\" অ্যাক্যুরেসি পেতে \n", + "print(scores.mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.95999999999999996, 0.95333333333333337, 0.96666666666666656, 0.96666666666666656, 0.96666666666666679, 0.96666666666666679, 0.96666666666666679, 0.96666666666666679, 0.97333333333333338, 0.96666666666666679, 0.96666666666666679, 0.97333333333333338, 0.98000000000000009, 0.97333333333333338, 0.97333333333333338, 0.97333333333333338, 0.97333333333333338, 0.98000000000000009, 0.97333333333333338, 0.98000000000000009, 0.96666666666666656, 0.96666666666666656, 0.97333333333333338, 0.95999999999999996, 0.96666666666666656, 0.95999999999999996, 0.96666666666666656, 0.95333333333333337, 0.95333333333333337, 0.95333333333333337]\n" + ] + } + ], + "source": [ + "# \"কে নিয়ারেস্ট নেইবার\" কতো হলে মডেলটা অপটিমাইজড হয়? ১ থেকে ৩১ পর্যন্ত \n", + "# এখানে \"কে ফোল্ড\" ক্রস ভ্যালিডেশন = ১০ মানে cv=10\n", + "k_range = list(range(1, 31))\n", + "k_scores = []\n", + "for k in k_range:\n", + " knn = KNeighborsClassifier(n_neighbors=k)\n", + " scores = cross_val_score(knn, X, y, cv=10, scoring='accuracy')\n", + " k_scores.append(scores.mean())\n", + "print(k_scores)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0,0.5,'Cross-Validated Accuracy')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ছবি আঁকতে হবে না? %matplotlib inline মানে হচ্ছে ছবিটা জুপিটার নোটবুকের ভেতরেই দেখাবে \n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "# plot the value of K for KNN (x-axis) versus the cross-validated accuracy (y-axis)\n", + "# কে এর মান ফেললাম এক্স এক্সিসে, অ্যাক্যুরেসি ওয়াই এক্সিসে \n", + "plt.plot(k_range, k_scores)\n", + "plt.xlabel('Value of K for KNN')\n", + "plt.ylabel('Cross-Validated Accuracy')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### \"কে ফোল্ড\" ক্রস ভ্যালিডেশন: মডেল বাছাই " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "আগের ছবিটা দেখুন। খালি চোখে \"কে\" এর মান ০.৯৮ দেখছি এক্স এক্সিসে ১৩, ১৮ এবং ২০এ। দেখি মডেলে ব্যবহার করে। " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Goal:** Compare the best KNN model with logistic regression on the iris dataset\n", + "দুটো মডেল তুলনা করি এখানে। \"কে নিয়ারেস্ট নেইবার\" আর লজিস্টিক রিগ্রেশন, কোনটা ভালো? সঙ্গে n_neighbors=20 এবং cv=10।" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.98\n" + ] + } + ], + "source": [ + "# \"কে নিয়ারেস্ট নেইবার\" সঙ্গে ১০ \"কে ফোল্ড\" ক্রস ভ্যালিডেশন\n", + "knn = KNeighborsClassifier(n_neighbors=20)\n", + "print(cross_val_score(knn, X, y, cv=10, scoring='accuracy').mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.953333333333\n" + ] + } + ], + "source": [ + "# লজিস্টিক রিগ্রেশন সঙ্গে একই ১০ \"কে ফোল্ড\" ক্রস ভ্যালিডেশন\n", + "from sklearn.linear_model import LogisticRegression\n", + "logreg = LogisticRegression()\n", + "print(cross_val_score(logreg, X, y, cv=10, scoring='accuracy').mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "দুটোর মধ্যে অ্যাক্যুরেসির বেশ ফারাক। এর মানে মডেল সিলেকশন একটা জরুরি বিষয় বটে। " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/kaggle/Iris_Species/dataframe.ipynb b/kaggle/Iris_Species/dataframe.ipynb new file mode 100644 index 0000000..0a3c5c8 --- /dev/null +++ b/kaggle/Iris_Species/dataframe.ipynb @@ -0,0 +1,182 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
AgeLocationName
024রাজশাহীজসিম
113ঢাকাকরিম
253রংপুরমিতা
333কুষ্টিয়াঅন্তরা
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" + ], + "text/plain": [ + " Age Location Name\n", + "0 24 রাজশাহী জসিম\n", + "1 13 ঢাকা করিম\n", + "2 53 রংপুর মিতা\n", + "3 33 কুষ্টিয়া অন্তরা" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# create a simple dataset of people\n", + "data = {'Name': [\"জসিম\", \"করিম\", \"মিতা\", \"অন্তরা\"],\n", + " 'Location' : [\"রাজশাহী\", \"ঢাকা\", \"রংপুর\", \"কুষ্টিয়া\"],\n", + " 'Age' : [24, 13, 53, 33]\n", + " }\n", + "\n", + "frame = pd.DataFrame(data)\n", + "# ডেটাফ্রেম দেখলেই আপনার মন ভালো হয়ে যাবে, একদম এক্সেল \n", + "display(frame)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "কোয়েরি চালাই একটু " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Age Location Name\n", + "2 53 রংপুর মিতা\n", + "3 33 কুষ্টিয়া অন্তরা" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(frame[frame.Age > 30])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/kaggle/Iris_Species/dimension_feature.ipynb b/kaggle/Iris_Species/dimension_feature.ipynb new file mode 100644 index 0000000..bdf14a4 --- /dev/null +++ b/kaggle/Iris_Species/dimension_feature.ipynb @@ -0,0 +1,202 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "dimension-feature.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "JUpH47tG97Kb", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# ডাইমেনশনালিটি রিডাকশন, ফীচার সিলেকশন, ফীচার ইম্পর্ট্যান্স\n", + "\n", + "রিয়েল ওয়ার্ল্ড সিনারিওতে যে কোন মেশিন লার্নিং সমস্যা আরো বেশি ঝামেলায় পড়ে - যখন তার ফিচার সংখ্যা অনেক হয়ে যায়। ফিচার সংখ্যা অনেক হওয়া সমস্যা নয়, সমস্যা হচ্ছে ফিচারগুলোর ভেতরে সব ফিচার কিন্তু মডেলের পারফরম্যান্স ভালো করে না। এছাড়াও এতো এতো ফিচার নিয়ে মডেলকে ট্রেনিং করানোটাও অনেক সময় সাপেক্ষ ব্যাপার। আবার ট্রেনিং করালাম, কিন্তু আউটকাম যা আশা করেছিলাম সেটা হলো না, তখন পুরো মডেলটাই বিপদে পড়ে। এই সমস্যাটা নাম হচ্ছে “দ্য কার্স অফ ডাইমেনশনালিটি।” অর্থাৎ বেশি ডাইমেনশনের বিপদ। সেজন্য দরকার ওই ফীচারগুলো, যা মডেল পারফরম্যান্সে সবচেয়ে বেশি ‘কন্ট্রিবিউট’ করে। \n", + "\n", + "এই সমস্যা থেকে উদ্ধার পাবার উপায় কি? সোজা হিসেবে বলা যায় ফিচার সিলেকশন এবং ফিচার ইম্পর্টেন্স। আমি অন্য গল্পে গেলাম না, কারণ এটা একটা বেসিক ধারণার বই। আমরা যদি দরকারি ফিচারগুলোকে ঠিকমতো সিলেক্ট করতে পারি তাদের ইম্পর্টেন্স অনুযায়ী, তাহলে কিন্তু ঝামেলা অনেকটাই কমে যায়। এই যে ধরুন, আইরিস ডেটাসেটে চারটা ফিচার। (রিয়েল ওয়ার্ল্ড সমস্যায় মিলিয়ন ফিচার নিয়ে কাজ করা এখন ‘কমনপ্লেস’ হয়ে যাচ্ছে)। এই চারটা ফিচারের মধ্যে কোন ফিচারগুলো আসলে আমাদের মডেলকে ভালো পারফর্মেন্স বুষ্ট দেবে, সেটা একটু দেখে আসি। \n", + "\n", + "মনে আছে, ডিসিশন ট্রি’র ছবিটার কথা? ইম্পরট্যান্ট ফীচারগুলো কিন্তু ডিসিশন ট্রি’র রুট নোডের আশপাশেই থাকে। ডেপ্থ ০, ১ এবং ২তে পেটাল দৈর্ঘ্যের জয়জয়কার। ডেপ্থগুলোকে গড় করলেই বোঝা যাবে। এর পাশাপাশি feature_importances_অ্যাট্রিবিউটের এর কাজ দেখে আসি। \n" + ] + }, + { + "metadata": { + "id": "lrdRtIsFE99V", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "5987f195-22c2-4749-c2ed-286316a9adc6" + }, + "cell_type": "code", + "source": [ + "from sklearn.datasets import load_iris\n", + "iris = load_iris()\n", + "X, y = iris.data, iris.target\n", + "X.shape" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(150, 4)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 1 + } + ] + }, + { + "metadata": { + "id": "dpVHgr88GqgG", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "56f2bf03-b719-4a67-b42e-c89aecd440fe" + }, + "cell_type": "code", + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "tree_clf = DecisionTreeClassifier(max_depth=2, random_state=42)\n", + "tree_clf.fit(X, y)\n", + "tree_clf.feature_importances_ " + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([0. , 0. , 0.56199095, 0.43800905])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 2 + } + ] + }, + { + "metadata": { + "id": "FkREKL0ZICu_", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "বোঝা যাচ্ছে চারটার মধ্যে দুটো ইম্পর্ট্যান্ট। জানা যাবে কোন দুটো?" + ] + }, + { + "metadata": { + "id": "G9h12FEAIkYL", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "bec34dbe-ddce-4d37-999c-c99e28ede257" + }, + "cell_type": "code", + "source": [ + "for name, score in zip(iris[\"feature_names\"], tree_clf.feature_importances_):\n", + " print(name, score)" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "sepal length (cm) 0.0\n", + "sepal width (cm) 0.0\n", + "petal length (cm) 0.5619909502262443\n", + "petal width (cm) 0.4380090497737556\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "uIV6_W3uIvDI", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "শেষের দুটো। এর মধ্যে পেটাল দৈর্ঘ্যের মান বেশি। " + ] + }, + { + "metadata": { + "id": "bTdaApw0HuFR", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "4a391bdd-a3b3-4e88-ea2a-5a3f451a493f" + }, + "cell_type": "code", + "source": [ + "from sklearn.feature_selection import SelectFromModel\n", + "model = SelectFromModel(tree_clf, prefit=True)\n", + "X_new = model.transform(X)\n", + "X_new.shape " + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(150, 2)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 6 + } + ] + }, + { + "metadata": { + "id": "MaXc85SeJdw8", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "শেষমেশ চারটার মধ্যে দুটোই আমাদের দরকারি ফিচার। সাধারণতঃ চারটা ফিচার থেকে কমাইনা আমরা। পরীক্ষা করে দেখলাম। " + ] + } + ] +} \ No newline at end of file diff --git a/kaggle/Iris_Species/exploratory-data-analysis.ipynb b/kaggle/Iris_Species/exploratory-data-analysis.ipynb new file mode 100644 index 0000000..13a5012 --- /dev/null +++ b/kaggle/Iris_Species/exploratory-data-analysis.ipynb @@ -0,0 +1,832 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## এক্সপ্লোরেটরি ডেটা অ্যানালাইসিস \n", + "রিভিশন ৪" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "আসলে আমাদের ডেটার ভেতরে কী আছে সেটা না জানলে এর থেকে প্রেডিকশন বের করবো কী করে? সেকারণে এই এক্সপ্লোরেশন। ডেটা নিয়ে একটু ঘাঁটাঘাঁটি করলে এর ভেতরের অনেক ধারণা পাওয়া যায় যেটা মডেল সিলেকশন অথবা ফীচারগুলো বুঝতে সুবিধা হয়। আগের চ্যাপ্টারের ভেতরে কিছুটা \"এক্সপ্লোরেটরি ডেটা অ্যানালাইসিস\" করলেও এখানে সেটাকে আরেকটু খোলাসা করছি। " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ডাটার শেপ, মানে কতোটা ইনস্ট্যান্স?" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "n_samples, n_features = iris.data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "150" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_samples" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_features" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of data: (150, 4)\n" + ] + } + ], + "source": [ + "print(\"Shape of data:\", iris['data'].shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "কোন ডাটা মিসিং নেই " + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(iris.target) == n_samples" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ফিচারগুলোর নাম \n", + "\n", + "ওপরের ছবিতে চারটা ফিচারের নাম দেখেছি। চলুন দেখি সেগুলো আমাদের ডাটাসেট অবজেক্টে। iris এর পর ডট নোটেশন ব্যবহার করে ডাকি একটা \"কী\" ভ্যালুকে। feature_names হচ্ছে আমাদের iris.keys() থেকে পাওয়া একটা অ্যাট্রিবিউট।" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['sepal length (cm)',\n", + " 'sepal width (cm)',\n", + " 'petal length (cm)',\n", + " 'petal width (cm)']" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris.feature_names" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n" + ] + } + ], + "source": [ + "print(iris['feature_names'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### টার্গেট অর্থাৎ কী প্রেডিক্ট করতে চাই আমরা?\n", + "\n", + "অনেকভাবেই করা সম্ভব। তবে print ফরম্যাটিং এ ভালো কাজ করে। " + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['setosa', 'versicolor', 'virginica'],\n", + " dtype='\n", + "\n" + ] + } + ], + "source": [ + "print(type(iris.data))\n", + "print(type(iris.target))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ফিচারের ম্যাট্রিক্স কি? (১ম ডাইমেনশন = অবজার্ভেশনের সংখ্যা, ২য় = ফিচারের সংখ্যা)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(150, 4)\n" + ] + } + ], + "source": [ + "print(iris.data.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "টার্গেট ম্যাট্রিক্স কি? (১ম ডাইমেনশন = লেবেল, টার্গেট, রেসপন্স)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(150,)\n" + ] + } + ], + "source": [ + "print(iris.target.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of target: (150,)\n" + ] + } + ], + "source": [ + "print(\"Shape of target:\", iris['target'].shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### সাইকিট-লার্ন এ ডাটা হ্যান্ডলিং এর নিয়ম \n", + "\n", + "১. এখানে \"ফিচার\" এবং \"রেসপন্স\" দুটো আলাদা অবজেক্ট \n", + "(আমাদের এখানে দেখুন, \"ফিচার\" এবং \"রেসপন্স\" মানে \"টার্গেট\" আলাদা অবজেক্ট)\n", + "\n", + "২. \"ফিচার\" এবং \"রেসপন্স\" দুটোকেই সংখ্যা হতে হবে \n", + "(আমাদের এখানে দুটোই সংখ্যার, দুটোর ম্যাট্রিক্স ডাইমেনশন হচ্ছে (১৫০ x ৪) এবং (১৫০ x ১)\n", + "\n", + "৩. \"ফিচার\" এবং \"রেসপন্স\" দুটোকেই \"নামপাই অ্যারে\" হতে হবে। \n", + "(আমাদের দুটো ফিচারই আছে \"নামপাই অ্যারে\"তে, বাকি ডাটা ডাটাসেট দরকার হলে সেটাকেও লোড করে নিতে হবে \"নামপাই অ্যারে\"তে)\n", + "\n", + "৪. \"ফিচার\" এবং \"রেসপন্স\" দুটোকেই স্পেসিফিক shape হতে হবে \n", + "\n", + "* ১৫০ x ৪ -> পুরো ডাটাসেট \n", + "* ১৫০ x ১ টার্গেটের জন্য \n", + "* ৪ x ১ ফিচারের জন্য \n", + "* আমরা ইচ্ছা করলে যেকোন ম্যাট্রিক্স পাল্টে নিতে পারি আমাদের দরকার মতো। যেমন np.tile(a, [4, 1]), মানে a হচ্ছে ম্যাট্রিক্স আর [4, 1] হচ্ছে ইনডেন্ট ম্যাট্রিক্স আরেক ডাইমেনশনে। " + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ফিচার ম্যাট্রিক্স স্টোর করছি বড় \"X\"এ, মনে আছে f(x)=y কথা? x ইনপুট হলে y আউটপুট \n", + "X = iris.data\n", + "\n", + "# রেসপন্স ভেক্টর রাখছি \"y\" তে \n", + "y = iris.target" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 5.1, 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1.3],\n", + " [ 6.7, 3.1, 4.4, 1.4],\n", + " [ 5.6, 3. , 4.5, 1.5],\n", + " [ 5.8, 2.7, 4.1, 1. ],\n", + " [ 6.2, 2.2, 4.5, 1.5],\n", + " [ 5.6, 2.5, 3.9, 1.1],\n", + " [ 5.9, 3.2, 4.8, 1.8],\n", + " [ 6.1, 2.8, 4. , 1.3],\n", + " [ 6.3, 2.5, 4.9, 1.5],\n", + " [ 6.1, 2.8, 4.7, 1.2],\n", + " [ 6.4, 2.9, 4.3, 1.3],\n", + " [ 6.6, 3. , 4.4, 1.4],\n", + " [ 6.8, 2.8, 4.8, 1.4],\n", + " [ 6.7, 3. , 5. , 1.7],\n", + " [ 6. , 2.9, 4.5, 1.5],\n", + " [ 5.7, 2.6, 3.5, 1. ],\n", + " [ 5.5, 2.4, 3.8, 1.1],\n", + " [ 5.5, 2.4, 3.7, 1. ],\n", + " [ 5.8, 2.7, 3.9, 1.2],\n", + " [ 6. , 2.7, 5.1, 1.6],\n", + " [ 5.4, 3. , 4.5, 1.5],\n", + " [ 6. , 3.4, 4.5, 1.6],\n", + " [ 6.7, 3.1, 4.7, 1.5],\n", + " [ 6.3, 2.3, 4.4, 1.3],\n", + " [ 5.6, 3. , 4.1, 1.3],\n", + " [ 5.5, 2.5, 4. , 1.3],\n", + " [ 5.5, 2.6, 4.4, 1.2],\n", + " [ 6.1, 3. , 4.6, 1.4],\n", + " [ 5.8, 2.6, 4. , 1.2],\n", + " [ 5. , 2.3, 3.3, 1. ],\n", + " [ 5.6, 2.7, 4.2, 1.3],\n", + " [ 5.7, 3. , 4.2, 1.2],\n", + " [ 5.7, 2.9, 4.2, 1.3],\n", + " [ 6.2, 2.9, 4.3, 1.3],\n", + " [ 5.1, 2.5, 3. , 1.1],\n", + " [ 5.7, 2.8, 4.1, 1.3],\n", + " [ 6.3, 3.3, 6. , 2.5],\n", + " [ 5.8, 2.7, 5.1, 1.9],\n", + " [ 7.1, 3. , 5.9, 2.1],\n", + " [ 6.3, 2.9, 5.6, 1.8],\n", + " [ 6.5, 3. , 5.8, 2.2],\n", + " [ 7.6, 3. , 6.6, 2.1],\n", + " [ 4.9, 2.5, 4.5, 1.7],\n", + " [ 7.3, 2.9, 6.3, 1.8],\n", + " [ 6.7, 2.5, 5.8, 1.8],\n", + " [ 7.2, 3.6, 6.1, 2.5],\n", + " [ 6.5, 3.2, 5.1, 2. ],\n", + " [ 6.4, 2.7, 5.3, 1.9],\n", + " [ 6.8, 3. , 5.5, 2.1],\n", + " [ 5.7, 2.5, 5. , 2. ],\n", + " [ 5.8, 2.8, 5.1, 2.4],\n", + " [ 6.4, 3.2, 5.3, 2.3],\n", + " [ 6.5, 3. , 5.5, 1.8],\n", + " [ 7.7, 3.8, 6.7, 2.2],\n", + " [ 7.7, 2.6, 6.9, 2.3],\n", + " [ 6. , 2.2, 5. , 1.5],\n", + " [ 6.9, 3.2, 5.7, 2.3],\n", + " [ 5.6, 2.8, 4.9, 2. ],\n", + " [ 7.7, 2.8, 6.7, 2. ],\n", + " [ 6.3, 2.7, 4.9, 1.8],\n", + " [ 6.7, 3.3, 5.7, 2.1],\n", + " [ 7.2, 3.2, 6. , 1.8],\n", + " [ 6.2, 2.8, 4.8, 1.8],\n", + " [ 6.1, 3. , 4.9, 1.8],\n", + " [ 6.4, 2.8, 5.6, 2.1],\n", + " [ 7.2, 3. , 5.8, 1.6],\n", + " [ 7.4, 2.8, 6.1, 1.9],\n", + " [ 7.9, 3.8, 6.4, 2. ],\n", + " [ 6.4, 2.8, 5.6, 2.2],\n", + " [ 6.3, 2.8, 5.1, 1.5],\n", + " [ 6.1, 2.6, 5.6, 1.4],\n", + " [ 7.7, 3. , 6.1, 2.3],\n", + " [ 6.3, 3.4, 5.6, 2.4],\n", + " [ 6.4, 3.1, 5.5, 1.8],\n", + " [ 6. , 3. , 4.8, 1.8],\n", + " [ 6.9, 3.1, 5.4, 2.1],\n", + " [ 6.7, 3.1, 5.6, 2.4],\n", + " [ 6.9, 3.1, 5.1, 2.3],\n", + " [ 5.8, 2.7, 5.1, 1.9],\n", + " [ 6.8, 3.2, 5.9, 2.3],\n", + " [ 6.7, 3.3, 5.7, 2.5],\n", + " [ 6.7, 3. , 5.2, 2.3],\n", + " [ 6.3, 2.5, 5. , 1.9],\n", + " [ 6.5, 3. , 5.2, 2. ],\n", + " [ 6.2, 3.4, 5.4, 2.3],\n", + " [ 5.9, 3. , 5.1, 1.8]])" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/kaggle/Iris_Species/linear-clf-final.ipynb b/kaggle/Iris_Species/linear-clf-final.ipynb new file mode 100644 index 0000000..00e5a11 --- /dev/null +++ b/kaggle/Iris_Species/linear-clf-final.ipynb @@ -0,0 +1,643 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "accelerator": "GPU", + "colab": { + "name": "linear-clf.ipynb", + "provenance": [], + "collapsed_sections": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ri6UiGU5T5aj" + }, + "source": [ + "# **সাইকিট-লার্ন দিয়ে একটা সহজ লিনিয়ার ক্লাসিফিকেশন **\n", + "\n", + "চারটার জায়গায় দুটো ফিচার, তিনটার জায়গায় দুটো টার্গেট ভ্যারিয়েবল" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "3sKNXPKNBqwO" + }, + "source": [ + "চারটার জায়গায় দুটো। প্রস্তাব - এটাকে দুটো দিয়ে দেখান না কেন? বুঝলাম - জিনিসটাকে আরো পানির মতো করতে হবে। আমাকে অনেকে বলেন, আইরিস ডেটাসেটে চারটা অ্যাট্রিবিউট। ফলে ডেটা ভিজ্যুয়ালাইজেশনে একটার ভেতরে আরেকটা চলে যায়। খালি চোখে ডেটার মধ্যে ফারাক বের করা তো দুস্কর। প্রস্তাবটা ভালো। এটা একটা বড় সমস্যাকে আরো রিফাইন করে আনবে আমাদের ভালোভাবে বুঝতে। \n", + "\n", + " সত্যি বলতে সেই আইডিয়াটা নিয়ে লিখেছেন বেশ কয়েকজন লেখক। তবে, এখানে আইডিয়াটা এলো আমার একটা প্রিয় বই থেকে, ২০১৩তে লেখা। লার্নিং সাইকিট-লার্ন:: মেশিন লার্নিং ইন পাইথন, রাউল গ্যারেটার। \"কী করবো সামনে?\" চ্যাপ্টারে দ্রষ্টব্য। \n", + "\n", + "আচ্ছা, তিনটা প্রজাতি না বের করে, একটা প্রজাতি বের করা যায় না? আরো, ভালো! তাহলে তো একটা প্রজাতি ভার্সেস ওই প্রজাতি নয়। মানে, প্রেডিক্ট করতে হবে - ধরুন, ফুলটা \"সেটোসা\" অথবা \"সেটোসা নয়\"! তাহলে তো জিনিসটা একদম পানি হয়ে যাবে। " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "gfRxmu0QT5a5" + }, + "source": [ + "## লোড করে নেই আইরিস ডেটাসেট " + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "c32_FG83T5a7", + "colab": {} + }, + "source": [ + "import sklearn\n", + "from sklearn import datasets\n", + "\n", + "iris = datasets.load_iris()\n", + "X_temp = iris.data\n", + "y_temp = iris.target" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "TrVDNHPBT5bL" + }, + "source": [ + "### ভাগ করে ফেলি টেস্ট এবং ট্রেনিং ডেটাসেট (ফিচার স্কেলিং সহ)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "zTvelXXOT5bM" + }, + "source": [ + "এখানে আমাদের কাজ হচ্ছে ডেটাসেটকে দুভাগে ভাগ করে ফেলা। ৭৫% ব্যবহার হবে আমাদের ক্লাসিফায়ারকে ট্রেনিং করাতে। ২৫% যাবে ইভ্যালুয়েট করতে। ৪টা ফিচারের জায়গায় আমরা ব্যবহার করবো ২টা মাত্র। সিপাল দৈর্ঘ্য এবং প্রস্থ। শুধুমাত্র সিপাল অংশ। \n", + "\n", + "এর পাশাপাশি আমরা ফিচার স্কেলিং করবো আমাদের ফিচারগুলোর ডেটা রেঞ্জ স্ট্যান্ডার্ডাইজ করার জন্য। প্রতিটা ফিচারের জন্য এটা সব ভ্যালুকে গড় করে সেটাকে বিয়োগ দেয় ওই ফিচার ভ্যালু থেকে। এরপর তার উত্তরকে ভাগ দেয় সেটার স্ট্যান্ডার্ড ডেভিয়েশন দিয়ে। আমাদের এই স্কেলিং এর পর প্রতিটা ফিচারের গড় হবে শূন্য। পাশাপাশি স্ট্যান্ডার্ড ডেভিয়েশন হচ্ছে ১। \n", + "\n", + "এর ফলে ভ্যালুগুলোর স্ট্যান্ডার্ডাইজেশন হয়ে আসে। এটা খানিকটা স্ট্যান্ডার্ড প্র্যাক্টিস হয়ে গেছে ইন্ডিপেন্ডেন্ট ফিচার/ভ্যারিয়েবলগুলোর রেঞ্জকে একটা স্কেলের মধ্যে নিয়ে আসা। এটাকে আমরা ডেটা নর্মালাইজেশন বলতে পারি। এটা আমরা করি ডেটা প্রি-প্রসেসিং এর সময়। \n", + "\n", + "ডেটার রেঞ্জ নিয়ে আমাদের যেহেতু কোন ফিল্টার নেই, সেকারণে একটা ডেটাসেটে বিক্ষিপ্ত ডেটা মেশিন লার্নিংকে বিপদে ফেলতে পারে। বড় বড় ভ্যালুগুলো ফাইনাল আউটকামে সমস্যা করে। আর সেকারণে মেশিন লার্নিং অ্যালগরিদমকে ভালোভাবে কাজ করানোর জন্য এই স্কেলিং দরকার পড়ে অনেক সময়। তবে, \"\"গ্রাডিয়েন্ট ডিসেন্ট\"\" কনভার্জেন্স ভালো কাজ করে স্কেলিং দিয়ে। মজার কথা হচ্ছে এক্স ভ্যালুগুলোকে প্লট করলে আগে এবং পরে একই জিনিস পাওয়া যায়। " + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "RknPx8MPT5bN", + "colab": {} + }, + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn import preprocessing\n", + "\n", + "# শুধুমাত্র প্রথম দুটো অ্যাট্রিবিউট নিয়ে আমাদের ডেটাসেট \n", + "X, y = X_temp[:, [0,1]], y_temp\n", + "# আমাদের টেস্টসেট হবে ২৫%, দৈবচয়নের ভিত্তিতে \n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=33)\n", + " \n", + "# ফিচারগুলোকে স্ট্যান্ডার্ডাইজ করছি এখানে \n", + "scaler = preprocessing.StandardScaler().fit(X_train)\n", + "X_train = scaler.transform(X_train)\n", + "X_test = scaler.transform(X_test)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "IRRoCZxmT5bQ" + }, + "source": [ + "চলুন, দেখি ফিচার স্কেলিং এর পর কি অবস্থা? এখানে গড় হচ্ছে \"০\", স্ট্যান্ডার্ড ডেভিয়েশন হচ্ছে \"১\"। ট্রেনিংসেটে ঠিকমতো হবে সবকিছু, তবে টেস্টসেটে ব্যাপারটা কাছাকাছি হবে। " + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "Xon7VkoGT5bR", + "outputId": "2b912fb5-aed9-4975-8443-308c01074409", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + } + }, + "source": [ + "import numpy as np\n", + "print ('Training set mean:{:.2f} and standard deviation:{:.2f}'.format(np.average(X_train),np.std(X_train)))\n", + "print ('Testing set mean:{:.2f} and standard deviation:{:.2f}'.format(np.average(X_test),np.std(X_test)))\n" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training set mean:0.00 and standard deviation:1.00\n", + "Testing set mean:0.13 and standard deviation:0.71\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "jFf9lDTeT5bV" + }, + "source": [ + "ফিচার স্কেলিং এর পর ট্রেনিং ডেটাকে প্লটিং করি। একই জিনিস। " + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "DPCGNVefT5bW", + "outputId": "9885b09b-f504-4c82-8d26-ea0dc7e37370", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 295 + } + }, + "source": [ + "# প্লটিং লাইব্রেরি লোড করে নেই \n", + "import matplotlib.pyplot as plt\n", + "\n", + "# তিন প্রজাতির তিনটা আলাদা রং, মার্কার সহ \n", + "colour_mk = [ ['red','s'], ['green','o'], ['blue','x']]\n", + "plt.figure('Training Data')\n", + "\n", + "# লুপে ফেলে দিলাম, x এবং y এক্সিসে \n", + "for i in range(len(colour_mk)):\n", + " xs = X_train[:, 0][y_train == i]\n", + " ys = X_train[:, 1][y_train == i]\n", + " plt.scatter(xs, ys, c=colour_mk[i][0], marker=colour_mk[i][1])\n", + "\n", + "# সাদা ব্যাকগ্রাউন্ড দরকার আমার, গুগল কোলাবে বাড়তি শেড দরকার নেই \n", + "# plt.rcParams['axes.facecolor'] = 'white'\n", + "plt.style.use('default')\n", + "plt.grid(c='grey')\n", + "\n", + "# প্লটিং প্যারামিটার \n", + "plt.title('Training instances, after scaling')\n", + "plt.legend(iris.target_names)\n", + "plt.xlabel('Sepal length')\n", + "plt.ylabel('Sepal width')\n", + "plt.show()\n" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "-fYy0VkkT5bb" + }, + "source": [ + "### একটা লিনিয়ার বাইনারি ক্লাসিফিকেশন " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "pfEKD6K5T5bc" + }, + "source": [ + "মানুষের মাথা প্যাটার্ন বুঝতে ওস্তাদ। এই প্লট থেকে কী বুঝতে পারছেন? ঠিক ধরেছেন। খালি চোখে সেটোসা প্রজাতিকে বোঝা যাচ্ছে একদম আলাদা করে। কেমন হয়, কমপ্লেক্সিটি এড়াতে আমরা যদি বের করতে চাই শুধুমাত্র সেটোসা প্রজাতি বের করতে চাই। মানে, প্রেডিক্ট করতে হবে হয় \"সেটোসা\" অথবা \"সেটোসা না\"? এখন আমাদের দুটো টার্গেট ভ্যারিয়েবল। সেকারণে এটাকে আমরা কনভার্ট করছি বাইনারি ক্লাসিফিকেশন টাস্কে। আমাদের দুটো টার্গেট। হয় \"০\" অথবা \"১\", তাহলে কী করতে হবে? \"১\" নম্বর এবং \"২\" নম্বর ক্লাসকে আমরা \"১\" বানিয়ে ফেলেছি। \n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "yLoZKBnCT5bd", + "outputId": "7240f679-1330-47c9-80f4-426d5c4bf9c6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 101 + } + }, + "source": [ + "import copy \n", + "y_train_setosa = copy.copy(y_train) \n", + "# আমাদের ট্রেনিংসেটের ১ এবং ২ ক্লাসকে ১ বানিয়ে ফেলছি \n", + "y_train_setosa[y_train_setosa > 0]=1\n", + "y_test_setosa = copy.copy(y_test)\n", + "y_test_setosa[y_test_setosa > 0]=1\n", + "# এখন দেখি ট্রেনিং টার্গেট ক্লাসগুলো কী কী?\n", + "print ('New training target classes:\\n{0}'.format(y_train_setosa))\n" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "New training target classes:\n", + "[1 0 1 1 1 0 0 1 0 1 0 0 1 1 0 1 1 1 1 1 0 0 1 0 0 1 1 1 1 1 1 1 0 0 1 1 0\n", + " 1 1 1 1 0 1 0 1 0 1 1 0 1 1 0 0 1 0 0 0 1 1 0 1 0 1 0 1 1 1 1 1 0 1 0 1 1\n", + " 0 0 0 0 1 1 0 1 1 1 1 0 0 1 1 1 0 1 1 0 1 1 1 1 1 0 1 0 0 0 1 1 1 1 1 1 1\n", + " 0]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mj_02w5mT5bi" + }, + "source": [ + "ছবিটা দেখে কী মনে হচ্ছে? একটা প্রজাতি একেবারে আলাদা। এটা আমাদের জন্য ভালো। \n", + "\n", + "আমরা যদি ভালোমতো করে ছবিটা দেখি - তাহলে \"সেটোসা\" প্রজাতিতে আমরা একেবারে আলাদা হাইপারপ্লেন এ দেখতে পাচ্ছি। অর্থাৎ একটা লাইন টেনে দুটো প্রজাতিকে আলাদা করতে পারছি। ব্যাপারটা কমন মেশিন লার্নিং কনসেপ্টে। আমাদের প্রশ্ন হচ্ছে নতুন মাপজোক দিলে সেটা থেকে বের করতে হবে নতুন জিনিসটা কোন প্রজাতির? এখন বাকি প্রজাতিগুলো যেহেতু একটা আরেকটার ভেতরে ঢুকে গেছে, সেকারনে ওই দুটোকে একটা প্রজাতি হিসেবে দেখাচ্ছি। \n", + "\n", + "যেহেতু, আমরা \"সেটোসা\"কে একেবারে একটা লাইন টেনে আলাদা করতে পারছি, সেকারণে এই জিনিসটাকে একটা লিনিয়ার ক্লাসিফিকেশন মডেলে পাঠাতে পারি। মানে, একটা সোজা লাইন টেনে দুটো টার্গেট ক্লাসকে আলাদা করবো এখানে। এটাকে আমরা বলতে পারি ফিচার স্পেসে একটা হাইপারপ্লেন। দুটো ফিচার স্পেসের মধ্যে লাইনটা ডিসিশন বাউন্ডারি। কে কোন প্রজাতির, সেটা নির্ভর করবে কে ওই লাইনটার কোন দিকে আছে। \n", + "\n", + "মনে আছে, আমাদের ওই এরর কমানোর কথা? লিস্ট স্কয়ার রিগ্রেশন, লস ফাংশন? যেটা আসলে বের করে আমাদের প্রতিটা ইনস্ট্যান্স থেকে ডিসিশন বাউন্ডারি কতো দুরে। এখানে আমাদের এই অ্যালগরিদম হাইপারপ্লেনের \"কোএফিসিয়েন্ট\" জানবে লসকে কমিয়ে। এখানে আমরা ইন্টারসেপ্টও জানবো সামনে। \n", + "\n", + "এ কারণে আমরা সাইকিট লার্ন থেকে `SGDClassifier` ব্যবহার করবো এই লিনিয়ার মডেল তৈরি করতে। আমাদের সাইকিট লার্নে \"SGDClassifier\" মডেল হিসেবে থাকলেও এটা আসলে ক্লাসিফায়ার নয়। বরং এটা একটা লিনিয়ার তবে এটাকে অপ্টিমাইজড করা হয়েছে স্টোকাস্টিক গ্র্যাডিয়েন্ট ডিসেন্ট দিয়ে। \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "5KCIy6waT5bm" + }, + "source": [ + "এই কাজ আমরা আগেও করেছি। \"linear_model\" কে ইম্পোর্ট করে নিয়ে আসছি sklearn থেকে। একটা ক্লাসিফায়ারের ইনস্ট্যান্স তৈরি করে হাইপারপ্যারামিটারকে বলছি \"লগ\" লস ফাংশন ব্যবহার করতে। এখানে ক্লাসিফায়ার হচ্ছে \"linear_model.SGDClassifier\"। এমুহুর্তে ব্যবহার করবো সব ডিফল্ট ভ্যালু। বেশি ঝামেলায় যাবো না। \n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "0jrNibUVT5bo", + "colab": {} + }, + "source": [ + "from sklearn import linear_model \n", + "clf = linear_model.SGDClassifier(loss='log', random_state=42)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "djVcNsMYT5bv" + }, + "source": [ + "এখন কি বাকি? ট্রেনিং করানো। ফিট মেথড কল করছি আমাদের ক্লাসিফায়ারকে ট্রেনিং করানোর জন্য। এখানে আমাদের ট্রেনিং ডেটা হচ্ছে \"সেটোসা\" সেট। \n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "h9Tf4RvNT5bx", + "outputId": "05109615-5878-442d-ffce-dbf0f07e5968", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 118 + } + }, + "source": [ + "clf.fit(X_train, y_train_setosa)" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n", + " early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n", + " l1_ratio=0.15, learning_rate='optimal', loss='log', max_iter=1000,\n", + " n_iter_no_change=5, n_jobs=None, penalty='l2', power_t=0.5,\n", + " random_state=42, shuffle=True, tol=0.001, validation_fraction=0.1,\n", + " verbose=0, warm_start=False)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "dVVYycW3T5b0" + }, + "source": [ + "লিনিয়ার মডেল। মনে আছে \"y = mx + b\" এর কথা? নাহ, অংক পিছু ছাড়ছেই না, আমাদেরকে m এবং b পেতে হবে। মানে, clf.coef_ এবং clf.intercept_ ছাড়া আমাদের গতি নেই। \n", + "\n", + "\n", + "এখন এই সমীকরণকে y = mx + b ধারণায় লিখলে কেমন দেখা যাবে?" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "94ScDMvbT5b1", + "outputId": "4a52e7ed-2c14-4ad4-c105-959ce00bbfbe", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "print (clf.coef_,clf.intercept_)\n" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[[ 21.76180378 -10.51985219]] [13.90763026]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "bg1L0kSLT5b5" + }, + "source": [ + "এখন তো ডিসিশন বাউন্ডারি আঁকাই যায়, কি বলুন? কোড দিলাম না ইচ্ছে করে। মেইন লিংকে পাওয়া যাবে। \n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OhODP9vIRYxk", + "colab_type": "text" + }, + "source": [ + "## এখানে কোড\n", + "\n", + "ধন্যবাদ \"A Gentle Introduction to Machine Learning with Python and Scikit-learn\" বইটাকে। ষষ্ঠ অধ্যায় দেখুন।" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5_JvEW9JReec", + "colab_type": "code", + "outputId": "0ef65ae8-0904-4bda-c2a9-1dcd4eb9bb1c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + } + }, + "source": [ + "x_min, x_max = X_train[:, 0].min() - .5, X_train[:, 0].max() + .5\n", + "y_min, y_max = X_train[:, 1].min() - .5, X_train[:, 1].max() + .5\n", + "xs = np.arange(x_min, x_max, 0.5)\n", + "\n", + "fig,axes = plt.subplots()\n", + "\n", + "axes.set_aspect('equal')\n", + "axes.set_title('Setosa species classification')\n", + "axes.set_xlabel('Sepal length')\n", + "axes.set_ylabel('Sepal width')\n", + "axes.set_xlim(x_min, x_max)\n", + "axes.set_ylim(y_min, y_max)\n", + "\n", + "plt.sca(axes)\n", + "\n", + "plt.scatter(X_train[:, 0][y_train == 0], X_train[:, 1][y_train == 0], c='red', marker='s')\n", + "plt.scatter(X_train[:, 0][y_train == 1], X_train[:, 1][y_train == 1], c='black', marker='x')\n", + "\n", + "ys = (-clf.intercept_[0]- xs * clf.coef_[0, 0]) / clf.coef_[0, 1]\n", + "\n", + "plt.plot(xs, ys)\n", + "plt.show()" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ViC_b6UrT5b-" + }, + "source": [ + "ঠিক ধরেছেন। এই নীল/কালো রেখাটাই হচ্ছে আমাদের ডিসিশন বাউন্ডারি। প্রতিবার ৩০.০৭ x \"সিপাল দৈর্ঘ্য\" - ১৭.৭৮ x \"সিপাল প্রস্থ্য\" - ১৭.৩১ এর আউটপুট যখন শূন্য থেকে বড় হবে তখন সেটা হবে আইরিস সেটোসা, মানে ক্লাস ০। \n", + "\n", + "চলুন, একটা প্রেডিক্ট করি। ফুলটা কি সেটোসা কি না? যদি একটা ফুলের পেটাল প্রস্থ্য ৪.৬ এবং পেটাল দৈর্ঘ্য ৩.২ হয়, তাহলে প্রজাতিটা কি সেটোসা হবে কি হবে না? " + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "J3_a65KZ8Si6", + "outputId": "1d41c074-4803-42be-9d00-4d8487f79fc3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "print ('If the flower has 4.6 petal width and 3.2 petal length is a {}'.format(\n", + " iris.target_names[clf.predict(scaler.transform([[4.6, 3.2]]))]))" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "If the flower has 4.6 petal width and 3.2 petal length is a ['setosa']\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "gVKeWGJq9gZ4" + }, + "source": [ + "উত্তর: সেটোসা!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9dr7-Y2WWeHk", + "colab_type": "text" + }, + "source": [ + "## মজার বোনাস কাজ\n", + "\n", + "কোড বোঝার দরকার নেই শুরুতে" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Q_JYuiBdWqrP", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 861 + }, + "outputId": "bf47d8ba-4ca8-40e9-caff-984dfb3c3bb5" + }, + "source": [ + "from sklearn.tree import DecisionTreeClassifier, plot_tree\n", + "\n", + "# কিছু প্যারামিটার\n", + "n_classes = 3\n", + "plot_colors = \"ryb\"\n", + "plot_step = 0.02\n", + "\n", + "for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3],\n", + " [1, 2], [1, 3], [2, 3]]):\n", + " # দুটো করেসপন্ডিং ফিচার\n", + " X = iris.data[:, pair]\n", + " y = iris.target\n", + "\n", + " # ট্রেনিং\n", + " clf = DecisionTreeClassifier().fit(X, y)\n", + "\n", + " # ডিসিশন বাউন্ডারি প্লট\n", + " plt.subplot(2, 3, pairidx + 1)\n", + "\n", + " x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n", + " y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n", + " xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),\n", + " np.arange(y_min, y_max, plot_step))\n", + " plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)\n", + "\n", + " Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n", + " Z = Z.reshape(xx.shape)\n", + " cs = plt.contourf(xx, yy, Z, cmap=plt.cm.RdYlBu)\n", + "\n", + " plt.xlabel(iris.feature_names[pair[0]])\n", + " plt.ylabel(iris.feature_names[pair[1]])\n", + "\n", + " # ট্রেনিং পয়েন্ট প্লট\n", + " for i, color in zip(range(n_classes), plot_colors):\n", + " idx = np.where(y == i)\n", + " plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i],\n", + " cmap=plt.cm.RdYlBu, edgecolor='black', s=15)\n", + "\n", + "plt.suptitle(\"Decision surface of a decision tree using paired features\")\n", + "plt.legend(loc='lower right', borderpad=0, handletextpad=0)\n", + "plt.axis(\"tight\")\n", + "\n", + "plt.figure()\n", + "clf = DecisionTreeClassifier().fit(iris.data, iris.target)\n", + "plot_tree(clf, filled=True)\n", + "plt.show()" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file diff --git a/kaggle/Iris_Species/linear_clf.ipynb b/kaggle/Iris_Species/linear_clf.ipynb new file mode 100644 index 0000000..aab6f34 --- /dev/null +++ b/kaggle/Iris_Species/linear_clf.ipynb @@ -0,0 +1,488 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "linear-clf.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "ri6UiGU5T5aj", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**সাইকিট-লার্ন দিয়ে একটা সহজ লিনিয়ার ক্লাসিফিকেশন **\n", + "\n", + "চারটার জায়গায় দুটো ফিচার, তিনটার জায়গায় দুটো টার্গেট ভ্যারিয়েবল" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "3sKNXPKNBqwO" + }, + "cell_type": "markdown", + "source": [ + "চারটার জায়গায় দুটো। প্রস্তাব - এটাকে দুটো দিয়ে দেখান না কেন? বুঝলাম - জিনিসটাকে আরো পানির মতো করতে হবে। আমাকে অনেকে বলেন, আইরিস ডেটাসেটে চারটা অ্যাট্রিবিউট। ফলে ডেটা ভিজ্যুয়ালাইজেশনে একটার ভেতরে আরেকটা চলে যায়। খালি চোখে ডেটার মধ্যে ফারাক বের করা তো দুস্কর। প্রস্তাবটা ভালো। এটা একটা বড় সমস্যাকে আরো রিফাইন করে আনবে আমাদের ভালোভাবে বুঝতে। \n", + "\n", + " সত্যি বলতে সেই আইডিয়াটা নিয়ে লিখেছেন বেশ কয়েকজন লেখক। তবে, এখানে আইডিয়াটা এলো আমার একটা প্রিয় বই থেকে, ২০১৩তে লেখা। লার্নিং সাইকিট-লার্ন:: মেশিন লার্নিং ইন পাইথন, রাউল গ্যারেটার। \"কী করবো সামনে?\" চ্যাপ্টারে দ্রষ্টব্য। \n", + "\n", + "আচ্ছা, তিনটা প্রজাতি না বের করে, একটা প্রজাতি বের করা যায় না? আরো, ভালো! তাহলে তো একটা প্রজাতি ভার্সেস ওই প্রজাতি নয়। মানে, প্রেডিক্ট করতে হবে - ধরুন, ফুলটা \"সেটোসা\" অথবা \"সেটোসা নয়\"! তাহলে তো জিনিসটা একদম পানি হয়ে যাবে। " + ] + }, + { + "metadata": { + "id": "gfRxmu0QT5a5", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## লোড করে নেই আইরিস ডেটাসেট " + ] + }, + { + "metadata": { + "id": "c32_FG83T5a7", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import sklearn\n", + "from sklearn import datasets\n", + "\n", + "iris = datasets.load_iris()\n", + "X_temp = iris.data\n", + "y_temp = iris.target" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "TrVDNHPBT5bL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### ভাগ করে ফেলি টেস্ট এবং ট্রেনিং ডেটাসেট (ফিচার স্কেলিং সহ)" + ] + }, + { + "metadata": { + "id": "zTvelXXOT5bM", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "এখানে আমাদের কাজ হচ্ছে ডেটাসেটকে দুভাগে ভাগ করে ফেলা। ৭৫% ব্যবহার হবে আমাদের ক্লাসিফায়ারকে ট্রেনিং করাতে। ২৫% যাবে ইভ্যালুয়েট করতে। ৪টা ফিচারের জায়গায় আমরা ব্যবহার করবো ২টা মাত্র। সিপাল দৈর্ঘ্য এবং প্রস্থ। শুধুমাত্র সিপাল অংশ। \n", + "\n", + "এর পাশাপাশি আমরা ফিচার স্কেলিং করবো আমাদের ফিচারগুলোর ডেটা রেঞ্জ স্ট্যান্ডার্ডাইজ করার জন্য। প্রতিটা ফিচারের জন্য এটা সব ভ্যালুকে গড় করে সেটাকে বিয়োগ দেয় ওই ফিচার ভ্যালু থেকে। এরপর তার উত্তরকে ভাগ দেয় সেটার স্ট্যান্ডার্ড ডেভিয়েশন দিয়ে। আমাদের এই স্কেলিং এর পর প্রতিটা ফিচারের গড় হবে শূন্য। পাশাপাশি স্ট্যান্ডার্ড ডেভিয়েশন হচ্ছে ১। \n", + "\n", + "এর ফলে ভ্যালুগুলোর স্ট্যান্ডার্ডাইজেশন হয়ে আসে। এটা খানিকটা স্ট্যান্ডার্ড প্র্যাক্টিস হয়ে গেছে ইন্ডিপেন্ডেন্ট ফিচার/ভ্যারিয়েবলগুলোর রেঞ্জকে একটা স্কেলের মধ্যে নিয়ে আসা। এটাকে আমরা ডেটা নর্মালাইজেশন বলতে পারি। এটা আমরা করি ডেটা প্রি-প্রসেসিং এর সময়। \n", + "\n", + "ডেটার রেঞ্জ নিয়ে আমাদের যেহেতু কোন ফিল্টার নেই, সেকারণে একটা ডেটাসেটে বিক্ষিপ্ত ডেটা মেশিন লার্নিংকে বিপদে ফেলতে পারে। বড় বড় ভ্যালুগুলো ফাইনাল আউটকামে সমস্যা করে। আর সেকারণে মেশিন লার্নিং অ্যালগরিদমকে ভালোভাবে কাজ করানোর জন্য এই স্কেলিং দরকার পড়ে অনেক সময়। তবে, \"\"গ্রাডিয়েন্ট ডিসেন্ট\"\" কনভার্জেন্স ভালো কাজ করে স্কেলিং দিয়ে। মজার কথা হচ্ছে এক্স ভ্যালুগুলোকে প্লট করলে আগে এবং পরে একই জিনিস পাওয়া যায়। " + ] + }, + { + "metadata": { + "id": "RknPx8MPT5bN", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn import preprocessing\n", + "\n", + "# শুধুমাত্র প্রথম দুটো অ্যাট্রিবিউট নিয়ে আমাদের ডেটাসেট \n", + "X, y = X_temp[:, [0,1]], y_temp\n", + "# আমাদের টেস্টসেট হবে ২৫%, দৈবচয়নের ভিত্তিতে \n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=33)\n", + " \n", + "# ফিচারগুলোকে স্ট্যান্ডার্ডাইজ করছি এখানে \n", + "scaler = preprocessing.StandardScaler().fit(X_train)\n", + "X_train = scaler.transform(X_train)\n", + "X_test = scaler.transform(X_test)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "IRRoCZxmT5bQ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "চলুন, দেখি ফিচার স্কেলিং এর পর কি অবস্থা? এখানে গড় হচ্ছে \"০\", স্ট্যান্ডার্ড ডেভিয়েশন হচ্ছে \"১\"। ট্রেনিংসেটে ঠিকমতো হবে সবকিছু, তবে টেস্টসেটে ব্যাপারটা কাছাকাছি হবে। " + ] + }, + { + "metadata": { + "id": "Xon7VkoGT5bR", + "colab_type": "code", + "outputId": "5ead9da0-71c7-4f2e-95c5-6862ebec473d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + } + }, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "print ('Training set mean:{:.2f} and standard deviation:{:.2f}'.format(np.average(X_train),np.std(X_train)))\n", + "print ('Testing set mean:{:.2f} and standard deviation:{:.2f}'.format(np.average(X_test),np.std(X_test)))\n" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training set mean:0.00 and standard deviation:1.00\n", + "Testing set mean:0.13 and standard deviation:0.71\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "jFf9lDTeT5bV", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "ফিচার স্কেলিং এর পর ট্রেনিং ডেটাকে প্লটিং করি। একই জিনিস। " + ] + }, + { + "metadata": { + "id": "DPCGNVefT5bW", + "colab_type": "code", + "outputId": "e853a9f3-480a-4dea-86fe-630cf69b9618", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 377 + } + }, + "cell_type": "code", + "source": [ + "# প্লটিং লাইব্রেরি লোড করে নেই \n", + "import matplotlib.pyplot as plt\n", + "\n", + "# তিন প্রজাতির তিনটা আলাদা রং, মার্কার সহ \n", + "colour_mk = [ ['red','s'], ['green','o'], ['blue','x']]\n", + "plt.figure('Training Data')\n", + "\n", + "# লুপে ফেলে দিলাম, x এবং y এক্সিসে \n", + "for i in range(len(colour_mk)):\n", + " xs = X_train[:, 0][y_train == i]\n", + " ys = X_train[:, 1][y_train == i]\n", + " plt.scatter(xs, ys, c=colour_mk[i][0], marker=colour_mk[i][1])\n", + "\n", + "# সাদা ব্যাকগ্রাউন্ড দরকার আমার, গুগল কোলাবে বাড়তি শেড দরকার নেই \n", + "# plt.rcParams['axes.facecolor'] = 'white'\n", + "plt.style.use('default')\n", + "plt.grid(c='grey')\n", + "\n", + "# প্লটিং প্যারামিটার \n", + "plt.title('Training instances, after scaling')\n", + "plt.legend(iris.target_names)\n", + "plt.xlabel('Sepal length')\n", + "plt.ylabel('Sepal width')\n", + "plt.show()\n" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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7ACA0tObIWU79mopH4EREZLOcnYHY2FK9ZbGxTU+yYvVrCiZwIiKyWWVlQFSU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\nphQ3sMZyoGKXO7Xlco6myCnJRlb5JSB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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "-fYy0VkkT5bb", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### একটা লিনিয়ার বাইনারি ক্লাসিফিকেশন " + ] + }, + { + "metadata": { + "id": "pfEKD6K5T5bc", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "মানুষের মাথা প্যাটার্ন বুঝতে ওস্তাদ। এই প্লট থেকে কী বুঝতে পারছেন? ঠিক ধরেছেন। খালি চোখে সেটোসা প্রজাতিকে বোঝা যাচ্ছে একদম আলাদা করে। কেমন হয়, কমপ্লেক্সিটি এড়াতে আমরা যদি বের করতে চাই শুধুমাত্র সেটোসা প্রজাতি বের করতে চাই। মানে, প্রেডিক্ট করতে হবে হয় \"সেটোসা\" অথবা \"সেটোসা না\"? এখন আমাদের দুটো টার্গেট ভ্যারিয়েবল। সেকারণে এটাকে আমরা কনভার্ট করছি বাইনারি ক্লাসিফিকেশন টাস্কে। আমাদের দুটো টার্গেট। হয় \"০\" অথবা \"১\", তাহলে কী করতে হবে? \"১\" নম্বর এবং \"২\" নম্বর ক্লাসকে আমরা \"১\" বানিয়ে ফেলেছি। \n", + "\n" + ] + }, + { + "metadata": { + "id": "yLoZKBnCT5bd", + "colab_type": "code", + "outputId": "b98e1fa2-654a-48ee-9b42-fba39dd88b49", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 101 + } + }, + "cell_type": "code", + "source": [ + "import copy \n", + "y_train_setosa = copy.copy(y_train) \n", + "# আমাদের ট্রেনিংসেটের ১ এবং ২ ক্লাসকে ১ বানিয়ে ফেলছি \n", + "y_train_setosa[y_train_setosa > 0]=1\n", + "y_test_setosa = copy.copy(y_test)\n", + "y_test_setosa[y_test_setosa > 0]=1\n", + "# এখন দেখি ট্রেনিং টার্গেট ক্লাসগুলো কী কী?\n", + "print ('New training target classes:\\n{0}'.format(y_train_setosa))\n" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "New training target classes:\n", + "[1 0 1 1 1 0 0 1 0 1 0 0 1 1 0 1 1 1 1 1 0 0 1 0 0 1 1 1 1 1 1 1 0 0 1 1 0\n", + " 1 1 1 1 0 1 0 1 0 1 1 0 1 1 0 0 1 0 0 0 1 1 0 1 0 1 0 1 1 1 1 1 0 1 0 1 1\n", + " 0 0 0 0 1 1 0 1 1 1 1 0 0 1 1 1 0 1 1 0 1 1 1 1 1 0 1 0 0 0 1 1 1 1 1 1 1\n", + " 0]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "mj_02w5mT5bi", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "ছবিটা দেখে কী মনে হচ্ছে? একটা প্রজাতি একেবারে আলাদা। এটা আমাদের জন্য ভালো। \n", + "\n", + "আমরা যদি ভালোমতো করে ছবিটা দেখি - তাহলে \"সেটোসা\" প্রজাতিতে আমরা একেবারে আলাদা হাইপারপ্লেন এ দেখতে পাচ্ছি। অর্থাৎ একটা লাইন টেনে দুটো প্রজাতিকে আলাদা করতে পারছি। ব্যাপারটা কমন মেশিন লার্নিং কনসেপ্টে। আমাদের প্রশ্ন হচ্ছে নতুন মাপজোক দিলে সেটা থেকে বের করতে হবে নতুন জিনিসটা কোন প্রজাতির? এখন বাকি প্রজাতিগুলো যেহেতু একটা আরেকটার ভেতরে ঢুকে গেছে, সেকারনে ওই দুটোকে একটা প্রজাতি হিসেবে দেখাচ্ছি। \n", + "\n", + "যেহেতু, আমরা \"সেটোসা\"কে একেবারে একটা লাইন টেনে আলাদা করতে পারছি, সেকারণে এই জিনিসটাকে একটা লিনিয়ার ক্লাসিফিকেশন মডেলে পাঠাতে পারি। মানে, একটা সোজা লাইন টেনে দুটো টার্গেট ক্লাসকে আলাদা করবো এখানে। এটাকে আমরা বলতে পারি ফিচার স্পেসে একটা হাইপারপ্লেন। দুটো ফিচার স্পেসের মধ্যে লাইনটা ডিসিশন বাউন্ডারি। কে কোন প্রজাতির, সেটা নির্ভর করবে কে ওই লাইনটার কোন দিকে আছে। \n", + "\n", + "মনে আছে, আমাদের ওই এরর কমানোর কথা? লিস্ট স্কয়ার রিগ্রেশন, লস ফাংশন? যেটা আসলে বের করে আমাদের প্রতিটা ইনস্ট্যান্স থেকে ডিসিশন বাউন্ডারি কতো দুরে। এখানে আমাদের এই অ্যালগরিদম হাইপারপ্লেনের \"কোএফিসিয়েন্ট\" জানবে লসকে কমিয়ে। এখানে আমরা ইন্টারসেপ্টও জানবো সামনে। \n", + "\n", + "এ কারণে আমরা সাইকিট লার্ন থেকে `SGDClassifier` ব্যবহার করবো এই লিনিয়ার মডেল তৈরি করতে। আমাদের সাইকিট লার্নে \"SGDClassifier\" মডেল হিসেবে থাকলেও এটা আসলে ক্লাসিফায়ার নয়। বরং এটা একটা লিনিয়ার তবে এটাকে অপ্টিমাইজড করা হয়েছে স্টোকাস্টিক গ্র্যাডিয়েন্ট ডিসেন্ট দিয়ে। \n" + ] + }, + { + "metadata": { + "id": "5KCIy6waT5bm", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "এই কাজ আমরা আগেও করেছি। \"linear_model\" কে ইম্পোর্ট করে নিয়ে আসছি sklearn থেকে। একটা ক্লাসিফায়ারের ইনস্ট্যান্স তৈরি করে হাইপারপ্যারামিটারকে বলছি \"লগ\" লস ফাংশন ব্যবহার করতে। এখানে ক্লাসিফায়ার হচ্ছে \"linear_model.SGDClassifier\"। এমুহুর্তে ব্যবহার করবো সব ডিফল্ট ভ্যালু। বেশি ঝামেলায় যাবো না। \n", + "\n" + ] + }, + { + "metadata": { + "id": "0jrNibUVT5bo", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from sklearn import linear_model \n", + "clf = linear_model.SGDClassifier(loss='log', random_state=42)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "djVcNsMYT5bv", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "এখন কি বাকি? ট্রেনিং করানো। ফিট মেথড কল করছি আমাদের ক্লাসিফায়ারকে ট্রেনিং করানোর জন্য। এখানে আমাদের ট্রেনিং ডেটা হচ্ছে \"সেটোসা\" সেট। \n" + ] + }, + { + "metadata": { + "id": "h9Tf4RvNT5bx", + "colab_type": "code", + "outputId": "e606bfcd-43e5-4896-f67d-4f315dbfa28d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 171 + } + }, + "cell_type": "code", + "source": [ + "clf.fit(X_train, y_train_setosa)\n", + "\n", + " " + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/stochastic_gradient.py:166: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n", + " FutureWarning)\n" + ], + "name": "stderr" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n", + " early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n", + " l1_ratio=0.15, learning_rate='optimal', loss='log', max_iter=None,\n", + " n_iter=None, n_iter_no_change=5, n_jobs=None, penalty='l2',\n", + " power_t=0.5, random_state=42, shuffle=True, tol=None,\n", + " validation_fraction=0.1, verbose=0, warm_start=False)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "metadata": { + "id": "dVVYycW3T5b0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "লিনিয়ার মডেল। মনে আছে \"y = mx + b\" এর কথা? নাহ, অংক পিছু ছাড়ছেই না, আমাদেরকে m এবং b পেতে হবে। মানে, clf.coef_ এবং clf.intercept_ ছাড়া আমাদের গতি নেই। \n", + "\n", + "\n", + "এখন এই সমীকরণকে y = mx + b ধারণায় লিখলে কেমন দেখা যাবে?" + ] + }, + { + "metadata": { + "id": "94ScDMvbT5b1", + "colab_type": "code", + "outputId": "ce1d81f0-8162-46fd-9ebd-1e47330ac0b1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "cell_type": "code", + "source": [ + "print (clf.coef_,clf.intercept_)\n" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[[ 31.0790909 -17.78632765]] [17.31337552]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "bg1L0kSLT5b5", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "এখন তো ডিসিশন বাউন্ডারি আঁকাই যায়, কি বলুন? কোড দিলাম না ইচ্ছে করে। মেইন লিংকে পাওয়া যাবে। \n", + "\n", + "![](https://drive.google.com/uc?export=view&id=1qa2eGu6sPNS7YqMSuB_07TBQnfu2s8B5)" + ] + }, + { + "metadata": { + "id": "ViC_b6UrT5b-", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "ঠিক ধরেছেন। এই নীল/কালো রেখাটাই হচ্ছে আমাদের ডিসিশন বাউন্ডারি। প্রতিবার ৩০.০৭ x \"সিপাল দৈর্ঘ্য\" - ১৭.৭৮ x \"সিপাল প্রস্থ্য\" - ১৭.৩১ এর আউটপুট যখন শূন্য থেকে বড় হবে তখন সেটা হবে আইরিস সেটোসা, মানে ক্লাস ০। \n", + "\n", + "চলুন, একটা প্রেডিক্ট করি। ফুলটা কি সেটোসা কি না? যদি একটা ফুলের পেটাল প্রস্থ্য ৪.৬ এবং পেটাল দৈর্ঘ্য ৩.২ হয়, তাহলে প্রজাতিটা কি সেটোসা হবে কি হবে না? " + ] + }, + { + "metadata": { + "id": "J3_a65KZ8Si6", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "6aa64133-9d0d-4812-99a0-8bef6f1bd9e3" + }, + "cell_type": "code", + "source": [ + "print ('If the flower has 4.6 petal width and 3.2 petal length is a {}'.format(\n", + " iris.target_names[clf.predict(scaler.transform([[4.6, 3.2]]))]))" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "If the flower has 4.6 petal width and 3.2 petal length is a ['setosa']\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "gVKeWGJq9gZ4", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "উত্তর: সেটোসা!" + ] + } + ] +} \ No newline at end of file diff --git a/kaggle/Iris_Species/model-evaluation1.ipynb b/kaggle/Iris_Species/model-evaluation1.ipynb new file mode 100644 index 0000000..fec5c9c --- /dev/null +++ b/kaggle/Iris_Species/model-evaluation1.ipynb @@ -0,0 +1,351 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# মডেলের কার্যকারীতা (ইভ্যালুয়েশন)\n", + "জুপিটার নোটবুকের এর লিংক https://github.com/raqueeb/ml-python/blob/master/model-evaluation1.ipynb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "এর আগে আলাপ করেছিলাম, আমাদের জানা দরকার - কোন ধরণের মডেল নিয়ে আমাদের কাজ ভালো হবে। পাশাপাশি ক্লাসিফায়ারের কোন টিউনিং প্যারামিটার নিয়ে কাজ করলে সবচেয়ে বেশি অ্যাক্যুরেসি আসবে, সেটা নিয়ে আলাপ করা দরকার। নিজের ডেটা দিয়ে ট্রেনিং করে 'আউট অফ স্যাম্পল ডেটা' (যেটা দিয়ে ট্রেনিং করাইনি) এর জন্য আমাদের মডেল কতটুকু তৈরি?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ১. এক ডেটাসেট দিয়ে ট্রেনিং এবং ইভাল্যুয়েট করানো (বর্জনীয়)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "১. পুরো আইরিস ডেটাসেট দিয়ে মডেলকে ট্রেনিং করি।\n", + "\n", + "২. একই ডেটাসেট দিয়ে ইভ্যালুয়েট করে দেখি কী হয় তার অ্যাক্যুরেসির অবস্থা। " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# আইরিস ডেটাসেটকে লোড করে নিচ্ছি\n", + "from sklearn.datasets import load_iris\n", + "iris = load_iris()\n", + "\n", + "# X এ ফীচার আর y এ রেসপন্স রাখছি \n", + "X = iris.data\n", + "y = iris.target" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### যদি \"কে-নিয়ারেস্ট নেইবার্স\" ক্লাসিফায়ারের নেইবার ৩ হয় " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# আগের মতো KNeighborsClassifier ইমপোর্ট করি \n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "# মডেলকে ইনস্ট্যানশিয়েট করলাম \n", + "knn = KNeighborsClassifier(n_neighbors=5)\n", + "# মডেলের মধ্যে সম্পৰ্ক তৈরি করি \n", + "knn.fit(X, y)\n", + "# X এর মধ্যে যে ভ্যালুগুলো আছে সেগুলোর রেসপন্স ভ্যালু প্রেডিক্ট করি \n", + "knn.predict(X)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "অনেক ভ্যালু, তাই না? আচ্ছা, প্রথম পাঁচটা ভ্যালু দেখি। " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 0, 0, 0])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# প্রথম পাঁচটা প্রেডিকশন \n", + "knn.predict(X)[0:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "150" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# y_pred তে প্রেডিক্টেড রেসপন্স ভ্যালুগুলোকে স্টোর করি \n", + "y_pred = knn.predict(X)\n", + "\n", + "# আমরা কতগুলো আইটেম প্রেডিক্ট করলাম?\n", + "len(y_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "প্রেডিকশনের কতোটুকু অ্যাক্যুরেসি এসেছে? এটা কিন্তু ইন্টারনাল ক্যালকুলেশন। পুরো ডেটাসেটের ওপর। এখানে score ফাংশন ব্যবহার করছি ফীচার আর টার্গেট রেসপন্সগুলোকে পাঠিয়ে। " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.96666666666666667" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn.score(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "এখানে একটু গল্প করি। এমুহুর্তে আমাদের মডেল প্রেডিক্ট করেছি জানা উত্তরের সাথে। ১৫০টা রেকর্ডের ১৫০টা টার্গেট ভ্যারিয়েবল (উত্তর) দেয়া আছে ডেটাসেটের সাথে। এখন knn.predict(X) দিয়ে বের করা প্রেডিক্টেড উত্তর মেলাতে হবে আসল উত্তরের সাথে। মেশিন লার্নিং কনভেনশন অনুযায়ী প্রেডিক্টেড উত্তরকে আমরা বলি \"y_pred\"। আচ্ছা, আমাদের আসল উত্তর স্টোর করা আছে কোথায়? ঠিক ধরেছেন \"y\" এ। মডেলের অ্যাক্যুরেসি জানবো কিভাবে? \"y\" এর সাথে \"y_pred\" তুলনা করলেই বোঝা যাবে। " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "আরেকটা গল্প করি। এটা পাইথন মেশিন লার্নিং গুরু সেবাস্টিয়ান রাখশা'এর একটা উত্তর। প্রিয় সাইট \"কোৱা\" থেকে নেয়া। এখানে y_true হচ্ছে সত্যি উত্তর আর y_pred হচ্ছে প্রেডিক্টেড উত্তর। y_pred এ স্টোর করছি আমাদের ক্লাস প্রেডিকশন। প্রতিটা ক্লাসের অ্যাক্যুরেসি বের করার জন্য দুটো মেথড ব্যবহার করা যেতে পারে। একটা হচ্ছে ক্লাসিফায়ারের স্কোর মেথড মানে knn.score(X, y) আরেকটা accuracy_score(X, y)। নিচের উদাহরণে y_true হচ্ছে আসল উত্তর, আর y_pred হচ্ছে প্রেডিকশন। নিচের উদাহরণটা দেখুন। y_true সত্যিকারের ডেটা থেকে প্রেডিক্টেড y_pred এর মধ্যে ১০টা ভ্যালুর মধ্যে একটাই ভুল হয়েছে। সেকারণে accuracy_score হচ্ছে ৯০%।" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.90000000000000002" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import accuracy_score\n", + "import numpy as np\n", + "y_true = np.array([0, 0, 0, 1, 1, 1, 2, 2, 2, 2])\n", + "y_pred1 = np.array([0, 0, 0, 1, 1, 1, 2, 2 , 2, 0])\n", + "accuracy_score(y_true, y_pred1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "এখন আসি আমাদের আইরিস ডেটাসেটের অ্যাক্যুরেসিতে। এটা আসবে আমাদের কতো শতাংশ প্রেডিকশন (y_pred) সত্যিকারের ভ্যালু (y) এর সাথে মিলেছে। এখানে আমরা metrics মডিউল ইমপোর্ট করে নিয়ে আসছি sklearn থেকে। এরপর y, y_pred ক্লাসকে পাঠিয়ে দিচ্ছি accuracy_score এর কাছে ক্লাসিফায়ারের কার্যকারীতা মানে অ্যাক্যুরেসি বের করার জন্য। " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.966666666667\n" + ] + } + ], + "source": [ + "# compute classification accuracy for the logistic regression model\n", + "from sklearn import metrics\n", + "print(metrics.accuracy_score(y, y_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "তাই বলে কী এটা হবে না? যেহেতু ট্রেনিং এবং টেস্ট একই ডেটাসেটে, আমরা এই জিনিষকে বলতে পারি \"ট্রেনিং অ্যাক্যুরেসি\"। " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.96666666666666667" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "# print(\"Test set score: {:.2f}\".format(np.mean(y_pred == y)))\n", + "np.mean(y_pred == y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### যদি \"কে-নিয়ারেস্ট নেইবার্স\" ক্লাসিফায়ারের নেইবার ১ হয় " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "knn = KNeighborsClassifier(n_neighbors=1)\n", + "knn.fit(X, y)\n", + "y_pred = knn.predict(X)\n", + "print(metrics.accuracy_score(y, y_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "এখানে চিন্তার অংকে খোরাক আছে। অ্যাক্যুরেসি ১ মানে ১০০% ঠিক প্রেডিক্ট করতে পেরেছে মডেল। এটা প্রশ্ন ফাঁসের মতো জিনিস। সেটা আমরা চাইবো না। চাইবো এমন একটা জেনারেলাইজড মডেল, যেটা যেকোন নতুন ডেটা দিয়ে কাজ করতে পারবে ভালো অ্যাক্যুরেসি দিয়ে। এগুলো ট্রেনিং ডেটা দিয়ে \"ওভারফিটিং\" হয়ে যায়।" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "এখন একটা কাজ করি। বলুনতো এখানে কী ভুল আছে? আপনার সামনে রয়েছে ইন্টারনেট ব্রাউজার। গুগল করে দেখুন, কী বলতে চেয়েছি এখানে? নতুন রাস্তা দেখতে হবে কনফিউশন ম্যাট্রিক্স নিয়ে। কনফিউশন ম্যাট্রিক্স কেন দরকার? এখানে পুরোটাই ট্রেনিং ডেটা। " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[50, 0, 0],\n", + " [ 0, 50, 0],\n", + " [ 0, 0, 50]], dtype=int64)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#import confusion_matrix\n", + "from sklearn.metrics import confusion_matrix\n", + "confusion_matrix(y,y_pred)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/kaggle/Iris_Species/model-evaluation2.ipynb b/kaggle/Iris_Species/model-evaluation2.ipynb new file mode 100644 index 0000000..4c941fb --- /dev/null +++ b/kaggle/Iris_Species/model-evaluation2.ipynb @@ -0,0 +1,539 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## করণীয় ইভ্যালুয়েশন প্রসেস: ট্রেইন/টেস্ট ভাগ \n", + "জুপিটার নোটবুকের লিংক https://github.com/raqueeb/ml-python/blob/master/model-evaluation2.ipynb\n", + "\n", + "ডাউনলোড করে নিন নিজের ব্যবহারের জন্য, ধারণার জন্য ধন্যবাদ কেভিন মার্কামকে। ডেটাস্কুল। " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"মডেল ইভাল্যুয়েশনের ধারণা\" চ্যাপ্টারের দ্বিতীয় প্রস্তাবনা দেখুন। \n", + "\n", + "১. পুরো ডেটাসেটকে ভাগ করে ফেলি দুভাগে। ক. ট্রেনিং সেট খ. টেস্ট সেট।\n", + "\n", + "২. মডেলকে ট্রেনিং করাবো \"ট্রেনিং সেট\" দিয়ে। \n", + "\n", + "৩. মডেলকে টেস্ট করবো \"টেস্ট সেট\" দিয়ে। সেটাই ইভ্যালুয়েট করবে কেমন করছে মডেলটা। \n", + "\n", + "৪. আমাদের সাইকিট-লার্নে এই কাজ করার জন্য train_test_split নামে একটা ফাংশন তৈরি করে দেয়া হয়েছে কাজের সুবিধার্থে। শুধুমাত্র কনভেনশনটা জানলেই চলবে। " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "আইরিস ডেটাসেট নিয়ে কাজ করার আগে একটা উদাহরণ দেখি। সাইকিট লার্ন ডকুমেন্টেশন থেকে নেয়া। আগে আপনাদেরকে দেখিয়ে নিয়ে আসি X এবং y এর ভেতরে কী আছে? " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1],\n", + " [2, 3],\n", + " [4, 5],\n", + " [6, 7],\n", + " [8, 9]])" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "# ভুলেও বোঝার দরকার নেই কিভাবে আমরা X, y জেনারেট করলাম \n", + "X, y = np.arange(10).reshape((5, 2)), range(5)\n", + "# আমাদের দেখতে হবে কি আছে X এর ভেতরে?\n", + "X" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "range(0, 5)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# এখন দেখি কি আছে y এর ভেতর। \n", + "y" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 1, 2, 3, 4]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# এর মানে ০ থেকে ৫টা সংখ্যা, লিস্ট কমান্ড দিয়ে দেখি বরং \n", + "list(y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "এখন আসি কাজের কাজে। কষ্ট করে X, y ম্যানুয়ালি আলাদা না করে ডেকে নিয়ে আসি train_test_split ফাংশনকে। সাইকিট লার্নের model_selection মডিউল থেকে। আমি যদি আলাদা করে কিছু না বলি, তাহলে সে আমাদের এই ৫ লাইনের ডেটাকে ৭৫% ট্রেনিং আর ২৫% টেস্ট ডেটাসেটে ভাগ করবে। " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "একটু ভালো করে লক্ষ্য করলেই দেখবেন নিচের কমান্ডটা একটা সাইকিট লার্ন কনভেনশন। এই স্টাইলে ফলো করে সবাই। এটাই ব্যবহার করবো আমরা। শুরুতে কপি করে চালাবো এই কনভেনশন। train_test_split পুরো ডেটাকে ট্রেনিং আর টেস্ট সেটে ভাগ করার আগে দৈবচয়নের মাধ্যমে (random_state) শাফল করে নেয় কাজের সুবিধার্থে। মনে আছে শুরুতে টার্গেট ভেক্টর 0,0,0 এর পর 1,1,1 অথবা 2,2,2 হওয়ার কারণে শাফল জরুরি। তবে, random_state=? ভ্যালু হিসেবে যা ব্যবহার করবেন সেটাকে এক রাখতে হবে পুরো এক্সারসাইজে। মনে রাখুন X ভাগ হবে X_train, X_test দুভাগে। সেখানে y হবে y_train, y_test দুভাগে। " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "চলুন দেখি X_train, X_test, y_train এবং y_test মধ্যে কী আছে? খেয়াল করুন কিভাবে পুরো ডেটাসেট ভাগ হয়েছে?" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[2, 3],\n", + " [8, 9],\n", + " [4, 5]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# ৫টা রেকর্ডের মধ্যে ৩টা এসেছে এখানে \n", + "X_train" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 4, 2]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# টার্গেট ভেক্টর আসতে হবে ওই ৩টাই \n", + "y_train" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1],\n", + " [6, 7]])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_test" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 3]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_test" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[0, 1, 2], [3, 4]]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_test_split(y, shuffle=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "দেখেছেন তো কিভাবে পুরো ডেটাসেট ভাগ হয়ে গেছে? এখন আসি আইরিস ডেটাসেটে। শুরুতে আগের গল্প। পপুলেট করে নেই ফিচার আর টার্গেট রেসপন্স। " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ধাপ ১" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# শুরুতে লোড করে নেই আইরিস ডেটাসেট \n", + "from sklearn.datasets import load_iris\n", + "iris = load_iris()\n", + "\n", + "# ফিচার আর টার্গেট রেসপন্স চলে যাচ্ছে X এবং y\n", + "X = iris.data\n", + "y = iris.target" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(150, 4)\n", + "(150,)\n" + ] + } + ], + "source": [ + "# train_test_split চালানোর আগে অ্যারেগুলোর সংখ্যা দেখে রাখি \n", + "print(X.shape)\n", + "print(y.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ধাপ ২" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ইমপোর্ট করছি train_test_split ফাংশনকে \n", + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "এই জিনিস থেকে কী পাবো আমরা?\n", + "\n", + "১. আলাদা আলাদা ডেটা দিয়ে মডেলকে ট্রেইন এবং টেস্ট করানো যাবে।\n", + "\n", + "২. টেস্ট সেটের 'রেসপন্স ভ্যালু' আমরা যেহেতু জানি, সেজন্য সেটার পারফরম্যান্স জানা যাবে। \n", + "\n", + "৩. টেস্টিং অ্যাক্যুরেসি ভালো হবে যখন দুটো আলাদা আলাদা ডেটাসেট। মডেলটা 'জেনারেলাইজড' হলো নতুন আউট অফ স্যাম্পল ডেটা নিয়ে কাজ করার জন্য।\n", + "\n", + "৪. ডিফল্ট সেটিংস ধরে রেকর্ডকে ভাগ করে ৭৫% ডেটাকে ট্রেনিং আর ২৫% ডেটাকে টেস্ট ডেটাসেটে ভাগ হয়ে যাবে। ৭৫% হচ্ছে ১১২টা রেকর্ড। ২৫% হচ্ছে ৩৮টা রেকর্ড।" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(112, 4)\n", + "(38, 4)\n" + ] + } + ], + "source": [ + "# নতুন X অবজেক্টগুলোর রেকর্ড সংখ্যা \n", + "print(X_train.shape)\n", + "print(X_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(112,)\n", + "(38,)\n" + ] + } + ], + "source": [ + "# নতুন y অবজেক্টগুলোর রেকর্ড সংখ্যা \n", + "print(y_train.shape)\n", + "print(y_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ধরুন, আপনার বন্ধু নাছোড়বান্দা। সে ডিফল্ট সেটিংস নিয়ে সন্তুষ্ট নয়। তার কথা হচ্ছে ট্রেনিং আর টেস্ট সেট ভাগ করতে চায় ৬০-৪০% ভাগে। তার জন্য আপনাকে যোগ করতে হবে test_size=0.4 মানে ৪০%। " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "দেখে নেই নতুন ভাগ। " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(60, 4)\n", + "(60,)\n" + ] + } + ], + "source": [ + "# নতুন X অবজেক্টগুলোর রেকর্ড সংখ্যা \n", + "print(X_test.shape)\n", + "print(y_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ধাপ ৩" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", + " metric_params=None, n_jobs=1, n_neighbors=3, p=2,\n", + " weights='uniform')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# আগের মতো KNeighborsClassifier ইমপোর্ট করি \n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "# মডেলকে ইনস্ট্যানশিয়েট করলাম \n", + "# যদি \"কে-নিয়ারেস্ট নেইবার্স\" ক্লাসিফায়ারের নেইবার ৩ হয়\n", + "knn = KNeighborsClassifier(n_neighbors=3)\n", + "# মডেলের মধ্যে সম্পৰ্ক তৈরি করি X_train এবং y_train দিয়ে\n", + "knn.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ধাপ ৪" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.966666666667\n" + ] + } + ], + "source": [ + "# প্রেডিকশন করছি টেস্ট সেট ধরে \n", + "y_pred = knn.predict(X_test)\n", + "# প্রেডিক্টেড রেসপন্স ভ্যালুর (y_pred) সাথে তুলনা করছি \n", + "# আসল রেসপন্স ভ্যালু (y_test)কে \n", + "# আগের মতো ইমপোর্ট করলাম metricsকে \n", + "from sklearn import metrics\n", + "print(metrics.accuracy_score(y_test, y_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### যদি \"কে-নিয়ারেস্ট নেইবার্স\" ক্লাসিফায়ারের নেইবার ৫ হয়" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.966666666667\n" + ] + } + ], + "source": [ + "knn = KNeighborsClassifier(n_neighbors=5)\n", + "knn.fit(X_train, y_train)\n", + "y_pred = knn.predict(X_test)\n", + "print(metrics.accuracy_score(y_test, y_pred))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/kaggle/Iris_Species/model-evaluation3.ipynb b/kaggle/Iris_Species/model-evaluation3.ipynb new file mode 100644 index 0000000..7e1a6d1 --- /dev/null +++ b/kaggle/Iris_Species/model-evaluation3.ipynb @@ -0,0 +1,306 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## নেইবারের সংখ্যা কতো হলে মডেলের অ্যাক্যুরেসি ভালো\n", + "জুপিটার নোটবুকের লিংক https://github.com/raqueeb/ml-python/blob/master/model-evaluation3.ipynb\n", + "\n", + "ডাউনলোড করে নিন নিজের ব্যবহারের জন্য, ধারণার জন্য ধন্যবাদ কেভিন মার্কামকে। ডেটাস্কুল। " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "নেইবারের সংখ্যা ৩ থেকে ৫ করার পর অ্যাক্যুরেসি 0.95 থেকে 0.96 হয়েছে। এখন নেইবারের সংখ্যা বার বার পাল্টে দেখা যেতে পারে কোথায় তার অ্যাক্যুরেসি সবচেয়ে বেশি। ম্যানুয়ালি না করে ফেলে দেই প্রোগ্রামিং লুপে। সেই বের করে দেবে কোথায় অ্যাক্যুরেসি ভালো। " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# যোগ করে নেই আগের অংশগুলো \n", + "# শুরুতে লোড করে নেই আইরিস ডেটাসেট \n", + "from sklearn.datasets import load_iris\n", + "iris = load_iris()\n", + "\n", + "# ফিচার আর টার্গেট রেসপন্স চলে যাচ্ছে X এবং y\n", + "X = iris.data\n", + "y = iris.target\n", + "\n", + "# STEP 1: split X and y into training and testing sets\n", + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=4)\n", + "\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "from sklearn import metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# আমরা K=1 থেকে K=25 লুপ চালিয়ে সেটাকে রেকর্ড করি \n", + "neighbors_settings = list(range(1, 26))\n", + "scores = []\n", + "for k in neighbors_settings:\n", + " knn = KNeighborsClassifier(n_neighbors=k)\n", + " knn.fit(X_train, y_train)\n", + " y_pred = knn.predict(X_test)\n", + " scores.append(metrics.accuracy_score(y_test, y_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ম্যানুয়ালি না দেখে k_range এবং scoresকে x, y এক্সিসে প্লট করি। ভিজ্যুয়ালাইজেশন ইজ দ্য কিং!" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0,0.5,'Testing Accuracy')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Matplotlibকে ইমপোর্ট করে নিয়ে আসি (আমাদের সাইন্টিফিক প্লটিং লাইব্রেরি)\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# আমাদের জুপিটার নোটবুকে ইনলাইনে দেখানোর জন্য \n", + "%matplotlib inline\n", + "\n", + "# আমরা K এবং \"testing accuracy\" এর সম্পর্ক প্লট করছি \n", + "plt.plot(neighbors_settings, scores)\n", + "# লেবেলের জন্য \n", + "plt.xlabel('Value of K for KNN')\n", + "plt.ylabel('Testing Accuracy')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.983333333333\n" + ] + } + ], + "source": [ + "# এখানে দেখছি ৭ থেকে ১৭ পর্যন্ত অ্যাক্যুরেসি ভালো, টেস্ট করছি ১০ দিয়ে \n", + "knn = KNeighborsClassifier(n_neighbors=10)\n", + "knn.fit(X_train, y_train)\n", + "y_pred = knn.predict(X_test)\n", + "print(metrics.accuracy_score(y_test, y_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**কী বুঝলাম এখানে?**\n", + "\n", + "১. \"কে-নিয়ারেস্ট নেইবার্স\" মডেলের কমপ্লেক্সিটি নির্ভর করছে K এর ভ্যালুর ওপর। ভ্যালু কম হলে কমপ্লেক্সিটি বেশি। \n", + "\n", + "২. মডেলের ট্রেনিং অ্যাক্যুরেসি বাড়ে মডেলের কমপ্লেক্সিটি বাড়লে। \n", + "\n", + "৩. টেস্টিং অ্যাক্যুরেসি পেনাল্টি করে মডেল খুব বেশি কমপ্লেক্সিটি অথবা খুব সহজ হয়ে গেলে। " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### কেমন হয় ১৫০ রেকর্ড ডেটাসেটের বাইরের স্যাম্পল দিয়ে প্রেডিক্ট করলে?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# নেইবার্স ১০ ধরলাম \n", + "knn = KNeighborsClassifier(n_neighbors=10)\n", + "\n", + "# মডেলকে ট্রেইন করতে হবে X এবং y দিয়ে (X_train, y_train নয়)\n", + "knn.fit(X, y)\n", + "\n", + "# প্রেডিক্ট করি নতুন স্যাম্পল দিয়ে \n", + "knn.predict([[3, 5, 4, 2]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "অথবা, যদি সরাসরি জানতে চাই আইরিস প্রজাতির নামটা?" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted target name: ['versicolor']\n" + ] + } + ], + "source": [ + "print(\"Predicted target name:\",\n", + " iris['target_names'][knn.predict([[3, 5, 4, 2]])])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### মডেলের মধ্যে কমপ্লেক্সিটি আর জেনেরালাইজেশন এর সম্পর্ক \n", + "\n", + "মনে আছে \"মডেলের জেনারেলাইজেশন, ওভার-ফিটিং এবং আন্ডার-ফিটিং\" চ্যাপ্টারের আলাপগুলোর কথা? আমরা একটা সম্পর্ক বের করতে চাচ্ছিলাম মডেল কমপ্লেক্সিটির সাথে 'জেনেরালাইজেশন' নিয়ে। এখানে আমরা আইরিস ডেটাসেটের ট্রেনিং আর টেস্ট ডেটাসেটের পারফরম্যান্স দেখি \"কে-নিয়ারেস্ট নেইবার্স\" এর নেইবারের সংখ্যা নিয়ে। \n", + "\n", + "এখানে ট্রেনিং এবং টেস্ট সেটের অ্যাক্যুরেসি দেখতে 'অ্যাক্যুরেসি' ফেলেছি ওয়াই এক্সিসে। \"কে-নিয়ারেস্ট নেইবার্স\" এর নেইবারের সংখ্যাকে দেখানো হয়েছে এক্স এক্সিসে। মনে আছে তো কম নেইবার মানে বেশি কমপ্লেক্স মডেল? একটা নেইবার নিয়ে ট্রেনিং সেট একদম পারফেক্ট। যখন নেইবার বাড়ছে, মডেল আস্তে আস্তে সিম্পলার মানে সহজ হচ্ছে। ফলে অ্যাক্যুরেসি কমছে। \n", + "\n", + "টেস্ট সেটের অ্যাক্যুরেসি কিন্তু কম একটা নেইবারে। তবে বেশি নেইবার হওয়াতে সেটা আরো কমছে। একটা নেইবারে মডেল অনেক কমপ্লেক্স তবে যতো বেশি নেইবার বাড়ছে অ্যাক্যুরেসি কমছে। তবে মাঝামাঝি জায়গায় মডেল ভালো করছে। " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Li/t38nU4SnlFXFwcOTk55Ofn+zoU5UXh4eHExcXV+3hNFrVoERLM4O7ab6ECW2hoKDab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\nW/uYMeawMSbdGNPDPirqX40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C\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "# আগের সব গল্প, শুধু বাড়িয়েছি n_neighbors এর সংখ্যা - ২৫ পর্যন্ত \n", + "\n", + "from sklearn.datasets import load_iris\n", + "iris = load_iris()\n", + "\n", + "X = iris.data\n", + "y = iris.target\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, stratify=y, random_state=4)\n", + "\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "training_accuracy = []\n", + "test_accuracy = []\n", + "# try n_neighbors from 1 to 25\n", + "neighbors_settings = range(1, 26)\n", + "\n", + "for n_neighbors in neighbors_settings:\n", + " # build the model\n", + " knn = KNeighborsClassifier(n_neighbors=n_neighbors)\n", + " knn.fit(X_train, y_train)\n", + " # record training set accuracy\n", + " training_accuracy.append(knn.score(X_train, y_train))\n", + " # record generalization accuracy\n", + " test_accuracy.append(knn.score(X_test, y_test))\n", + "\n", + "#নিচের প্লটটা দেখাচ্ছে ট্রেনিং এবং টেস্ট সেটের অ্যাক্যুরেসি \n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.plot(neighbors_settings, training_accuracy, label=\"training accuracy\")\n", + "plt.plot(neighbors_settings, test_accuracy, label=\"test accuracy\")\n", + "plt.ylabel(\"Accuracy\")\n", + "plt.xlabel(\"Numbers of neighbors\")\n", + "plt.legend()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/kaggle/Iris_Species/scikit-learn2-test.ipynb b/kaggle/Iris_Species/scikit-learn2-test.ipynb new file mode 100644 index 0000000..6972760 --- /dev/null +++ b/kaggle/Iris_Species/scikit-learn2-test.ipynb @@ -0,0 +1,697 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "iris = sns.load_dataset(\"iris\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.frame.DataFrame" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(iris)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
05.13.51.40.2setosa
14.93.01.40.2setosa
24.73.21.30.2setosa
34.63.11.50.2setosa
45.03.61.40.2setosa
55.43.91.70.4setosa
64.63.41.40.3setosa
75.03.41.50.2setosa
84.42.91.40.2setosa
94.93.11.50.1setosa
105.43.71.50.2setosa
114.83.41.60.2setosa
124.83.01.40.1setosa
134.33.01.10.1setosa
145.84.01.20.2setosa
155.74.41.50.4setosa
165.43.91.30.4setosa
175.13.51.40.3setosa
185.73.81.70.3setosa
195.13.81.50.3setosa
205.43.41.70.2setosa
215.13.71.50.4setosa
224.63.61.00.2setosa
235.13.31.70.5setosa
244.83.41.90.2setosa
255.03.01.60.2setosa
265.03.41.60.4setosa
275.23.51.50.2setosa
285.23.41.40.2setosa
294.73.21.60.2setosa
..................
1206.93.25.72.3virginica
1215.62.84.92.0virginica
1227.72.86.72.0virginica
1236.32.74.91.8virginica
1246.73.35.72.1virginica
1257.23.26.01.8virginica
1266.22.84.81.8virginica
1276.13.04.91.8virginica
1286.42.85.62.1virginica
1297.23.05.81.6virginica
1307.42.86.11.9virginica
1317.93.86.42.0virginica
1326.42.85.62.2virginica
1336.32.85.11.5virginica
1346.12.65.61.4virginica
1357.73.06.12.3virginica
1366.33.45.62.4virginica
1376.43.15.51.8virginica
1386.03.04.81.8virginica
1396.93.15.42.1virginica
1406.73.15.62.4virginica
1416.93.15.12.3virginica
1425.82.75.11.9virginica
1436.83.25.92.3virginica
1446.73.35.72.5virginica
1456.73.05.22.3virginica
1466.32.55.01.9virginica
1476.53.05.22.0virginica
1486.23.45.42.3virginica
1495.93.05.11.8virginica
\n", + "

150 rows × 5 columns

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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "0 5.1 3.5 1.4 0.2 setosa\n", + "1 4.9 3.0 1.4 0.2 setosa\n", + "2 4.7 3.2 1.3 0.2 setosa\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "4 5.0 3.6 1.4 0.2 setosa\n", + "5 5.4 3.9 1.7 0.4 setosa\n", + "6 4.6 3.4 1.4 0.3 setosa\n", + "7 5.0 3.4 1.5 0.2 setosa\n", + "8 4.4 2.9 1.4 0.2 setosa\n", + "9 4.9 3.1 1.5 0.1 setosa\n", + "10 5.4 3.7 1.5 0.2 setosa\n", + "11 4.8 3.4 1.6 0.2 setosa\n", + "12 4.8 3.0 1.4 0.1 setosa\n", + "13 4.3 3.0 1.1 0.1 setosa\n", + "14 5.8 4.0 1.2 0.2 setosa\n", + "15 5.7 4.4 1.5 0.4 setosa\n", + "16 5.4 3.9 1.3 0.4 setosa\n", + "17 5.1 3.5 1.4 0.3 setosa\n", + "18 5.7 3.8 1.7 0.3 setosa\n", + "19 5.1 3.8 1.5 0.3 setosa\n", + "20 5.4 3.4 1.7 0.2 setosa\n", + "21 5.1 3.7 1.5 0.4 setosa\n", + "22 4.6 3.6 1.0 0.2 setosa\n", + "23 5.1 3.3 1.7 0.5 setosa\n", + "24 4.8 3.4 1.9 0.2 setosa\n", + "25 5.0 3.0 1.6 0.2 setosa\n", + "26 5.0 3.4 1.6 0.4 setosa\n", + "27 5.2 3.5 1.5 0.2 setosa\n", + "28 5.2 3.4 1.4 0.2 setosa\n", + "29 4.7 3.2 1.6 0.2 setosa\n", + ".. ... ... ... ... ...\n", + "120 6.9 3.2 5.7 2.3 virginica\n", + "121 5.6 2.8 4.9 2.0 virginica\n", + "122 7.7 2.8 6.7 2.0 virginica\n", + "123 6.3 2.7 4.9 1.8 virginica\n", + "124 6.7 3.3 5.7 2.1 virginica\n", + "125 7.2 3.2 6.0 1.8 virginica\n", + "126 6.2 2.8 4.8 1.8 virginica\n", + "127 6.1 3.0 4.9 1.8 virginica\n", + "128 6.4 2.8 5.6 2.1 virginica\n", + "129 7.2 3.0 5.8 1.6 virginica\n", + "130 7.4 2.8 6.1 1.9 virginica\n", + "131 7.9 3.8 6.4 2.0 virginica\n", + "132 6.4 2.8 5.6 2.2 virginica\n", + "133 6.3 2.8 5.1 1.5 virginica\n", + "134 6.1 2.6 5.6 1.4 virginica\n", + "135 7.7 3.0 6.1 2.3 virginica\n", + "136 6.3 3.4 5.6 2.4 virginica\n", + "137 6.4 3.1 5.5 1.8 virginica\n", + "138 6.0 3.0 4.8 1.8 virginica\n", + "139 6.9 3.1 5.4 2.1 virginica\n", + "140 6.7 3.1 5.6 2.4 virginica\n", + "141 6.9 3.1 5.1 2.3 virginica\n", + "142 5.8 2.7 5.1 1.9 virginica\n", + "143 6.8 3.2 5.9 2.3 virginica\n", + "144 6.7 3.3 5.7 2.5 virginica\n", + "145 6.7 3.0 5.2 2.3 virginica\n", + "146 6.3 2.5 5.0 1.9 virginica\n", + "147 6.5 3.0 5.2 2.0 virginica\n", + "148 6.2 3.4 5.4 2.3 virginica\n", + "149 5.9 3.0 5.1 1.8 virginica\n", + "\n", + "[150 rows x 5 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/kaggle/Iris_Species/scikit-learn3-test.ipynb b/kaggle/Iris_Species/scikit-learn3-test.ipynb new file mode 100644 index 0000000..8e64877 --- /dev/null +++ b/kaggle/Iris_Species/scikit-learn3-test.ipynb @@ -0,0 +1,810 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.datasets import load_iris" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "iris = load_iris()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "iris_dataframe = pd.DataFrame(data= np.c_[iris['data'], iris['target']],\n", + " columns= iris['feature_names'] + ['target'])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "iris_dataframe['species'] = pd.Categorical.from_codes(iris.target, \n", + " iris.target_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)targetspecies
05.13.51.40.20.0setosa
14.93.01.40.20.0setosa
24.73.21.30.20.0setosa
34.63.11.50.20.0setosa
45.03.61.40.20.0setosa
55.43.91.70.40.0setosa
64.63.41.40.30.0setosa
75.03.41.50.20.0setosa
84.42.91.40.20.0setosa
94.93.11.50.10.0setosa
105.43.71.50.20.0setosa
114.83.41.60.20.0setosa
124.83.01.40.10.0setosa
134.33.01.10.10.0setosa
145.84.01.20.20.0setosa
155.74.41.50.40.0setosa
165.43.91.30.40.0setosa
175.13.51.40.30.0setosa
185.73.81.70.30.0setosa
195.13.81.50.30.0setosa
205.43.41.70.20.0setosa
215.13.71.50.40.0setosa
224.63.61.00.20.0setosa
235.13.31.70.50.0setosa
244.83.41.90.20.0setosa
255.03.01.60.20.0setosa
265.03.41.60.40.0setosa
275.23.51.50.20.0setosa
285.23.41.40.20.0setosa
294.73.21.60.20.0setosa
.....................
1206.93.25.72.32.0virginica
1215.62.84.92.02.0virginica
1227.72.86.72.02.0virginica
1236.32.74.91.82.0virginica
1246.73.35.72.12.0virginica
1257.23.26.01.82.0virginica
1266.22.84.81.82.0virginica
1276.13.04.91.82.0virginica
1286.42.85.62.12.0virginica
1297.23.05.81.62.0virginica
1307.42.86.11.92.0virginica
1317.93.86.42.02.0virginica
1326.42.85.62.22.0virginica
1336.32.85.11.52.0virginica
1346.12.65.61.42.0virginica
1357.73.06.12.32.0virginica
1366.33.45.62.42.0virginica
1376.43.15.51.82.0virginica
1386.03.04.81.82.0virginica
1396.93.15.42.12.0virginica
1406.73.15.62.42.0virginica
1416.93.15.12.32.0virginica
1425.82.75.11.92.0virginica
1436.83.25.92.32.0virginica
1446.73.35.72.52.0virginica
1456.73.05.22.32.0virginica
1466.32.55.01.92.0virginica
1476.53.05.22.02.0virginica
1486.23.45.42.32.0virginica
1495.93.05.11.82.0virginica
\n", + "

150 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n", + "0 5.1 3.5 1.4 0.2 \n", + "1 4.9 3.0 1.4 0.2 \n", + "2 4.7 3.2 1.3 0.2 \n", + "3 4.6 3.1 1.5 0.2 \n", + "4 5.0 3.6 1.4 0.2 \n", + "5 5.4 3.9 1.7 0.4 \n", + "6 4.6 3.4 1.4 0.3 \n", + "7 5.0 3.4 1.5 0.2 \n", + "8 4.4 2.9 1.4 0.2 \n", + "9 4.9 3.1 1.5 0.1 \n", + "10 5.4 3.7 1.5 0.2 \n", + "11 4.8 3.4 1.6 0.2 \n", + "12 4.8 3.0 1.4 0.1 \n", + "13 4.3 3.0 1.1 0.1 \n", + "14 5.8 4.0 1.2 0.2 \n", + "15 5.7 4.4 1.5 0.4 \n", + "16 5.4 3.9 1.3 0.4 \n", + "17 5.1 3.5 1.4 0.3 \n", + "18 5.7 3.8 1.7 0.3 \n", + "19 5.1 3.8 1.5 0.3 \n", + "20 5.4 3.4 1.7 0.2 \n", + "21 5.1 3.7 1.5 0.4 \n", + "22 4.6 3.6 1.0 0.2 \n", + "23 5.1 3.3 1.7 0.5 \n", + "24 4.8 3.4 1.9 0.2 \n", + "25 5.0 3.0 1.6 0.2 \n", + "26 5.0 3.4 1.6 0.4 \n", + "27 5.2 3.5 1.5 0.2 \n", + "28 5.2 3.4 1.4 0.2 \n", + "29 4.7 3.2 1.6 0.2 \n", + ".. ... ... ... ... \n", + "120 6.9 3.2 5.7 2.3 \n", + "121 5.6 2.8 4.9 2.0 \n", + "122 7.7 2.8 6.7 2.0 \n", + "123 6.3 2.7 4.9 1.8 \n", + "124 6.7 3.3 5.7 2.1 \n", + "125 7.2 3.2 6.0 1.8 \n", + "126 6.2 2.8 4.8 1.8 \n", + "127 6.1 3.0 4.9 1.8 \n", + "128 6.4 2.8 5.6 2.1 \n", + "129 7.2 3.0 5.8 1.6 \n", + "130 7.4 2.8 6.1 1.9 \n", + "131 7.9 3.8 6.4 2.0 \n", + "132 6.4 2.8 5.6 2.2 \n", + "133 6.3 2.8 5.1 1.5 \n", + "134 6.1 2.6 5.6 1.4 \n", + "135 7.7 3.0 6.1 2.3 \n", + "136 6.3 3.4 5.6 2.4 \n", + "137 6.4 3.1 5.5 1.8 \n", + "138 6.0 3.0 4.8 1.8 \n", + "139 6.9 3.1 5.4 2.1 \n", + "140 6.7 3.1 5.6 2.4 \n", + "141 6.9 3.1 5.1 2.3 \n", + "142 5.8 2.7 5.1 1.9 \n", + "143 6.8 3.2 5.9 2.3 \n", + "144 6.7 3.3 5.7 2.5 \n", + "145 6.7 3.0 5.2 2.3 \n", + "146 6.3 2.5 5.0 1.9 \n", + "147 6.5 3.0 5.2 2.0 \n", + "148 6.2 3.4 5.4 2.3 \n", + "149 5.9 3.0 5.1 1.8 \n", + "\n", + " target species \n", + "0 0.0 setosa \n", + "1 0.0 setosa \n", + "2 0.0 setosa \n", + "3 0.0 setosa \n", + "4 0.0 setosa \n", + "5 0.0 setosa \n", + "6 0.0 setosa \n", + "7 0.0 setosa \n", + "8 0.0 setosa \n", + "9 0.0 setosa \n", + "10 0.0 setosa \n", + "11 0.0 setosa \n", + "12 0.0 setosa \n", + "13 0.0 setosa \n", + "14 0.0 setosa \n", + "15 0.0 setosa \n", + "16 0.0 setosa \n", + "17 0.0 setosa \n", + "18 0.0 setosa \n", + "19 0.0 setosa \n", + "20 0.0 setosa \n", + "21 0.0 setosa \n", + "22 0.0 setosa \n", + "23 0.0 setosa \n", + "24 0.0 setosa \n", + "25 0.0 setosa \n", + "26 0.0 setosa \n", + "27 0.0 setosa \n", + "28 0.0 setosa \n", + "29 0.0 setosa \n", + ".. ... ... \n", + "120 2.0 virginica \n", + "121 2.0 virginica \n", + "122 2.0 virginica \n", + "123 2.0 virginica \n", + "124 2.0 virginica \n", + "125 2.0 virginica \n", + "126 2.0 virginica \n", + "127 2.0 virginica \n", + "128 2.0 virginica \n", + "129 2.0 virginica \n", + "130 2.0 virginica \n", + "131 2.0 virginica \n", + "132 2.0 virginica \n", + "133 2.0 virginica \n", + "134 2.0 virginica \n", + "135 2.0 virginica \n", + "136 2.0 virginica \n", + "137 2.0 virginica \n", + "138 2.0 virginica \n", + "139 2.0 virginica \n", + "140 2.0 virginica \n", + "141 2.0 virginica \n", + "142 2.0 virginica \n", + "143 2.0 virginica \n", + "144 2.0 virginica \n", + "145 2.0 virginica \n", + "146 2.0 virginica \n", + "147 2.0 virginica \n", + "148 2.0 virginica \n", + "149 2.0 virginica \n", + "\n", + "[150 rows x 6 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris_dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/kaggle/Iris_Species/seaborn.ipynb b/kaggle/Iris_Species/seaborn.ipynb new file mode 100644 index 0000000..c15c638 --- /dev/null +++ b/kaggle/Iris_Species/seaborn.ipynb @@ -0,0 +1,609 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ডেটা ভিজ্যুয়ালাইজেশন\n", + "গিটহাব লিংক https://github.com/raqueeb/ml-python/blob/master/seaborn.ipynb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "যেকোন মেশিন লার্নিং মডেল তৈরির আগে ডেটাকে হাতে নিয়ে দেখাটা একটা ভালো অভ্যেস। প্রথমতঃ ডাটার ভেতরে আমাদের উত্তরের ধারণাটা আছে কিনা সেটা জানলে আমাদের কষ্ট কমে যাবে। দ্বিতীয়তঃ এই সমস্যাটা আসলে মেশিন লার্নিং সমস্যা কি না সেটা জানলে আমাদের এই রাস্তায় হাটতে হয় না। একটা জিনিস দেখা গেছে - যখন আমরা আমাদের ডাটাকে দুটো (তিনটাও হতে পারে) এক্সিসে প্লট করি - তখন একটা মজার ব্যাপার ঘটে। মানুষ বুঝতে পারে ভেতরের ঘটনা।\n", + "\n", + "পৃথিবী কিন্তু প্যাটার্নে ভর্তি। আশেপাশে তাকান। প্রাকৃতিক হাজারো জিনিসের প্যাটার্ন, গাছ, মাটির স্তর, সূর্যের আলো, পানির ঢেউ দেখলেই বোঝা যায় অনেক কিছু। ফিজিক্স, ম্যাথ তো প্যাটার্ন ভর্তি। এই প্যাটার্নের ভেতর থেকে অসামঞ্জস্যতা বা তার কিছুটা 'পিকিউলারিটিজ' বের করাতে ডেটা প্লট একটা দরকারী জিনিস। কেমন হয়, আমাদের কিছু আইরিস ডেটাকে মাপামাপি করা হয়েছিলো ইঞ্চিতে যেখানে সেটা হবার কথা সেন্টিমিটারে? সেটাও ধরা পড়বে এই ডেটা ভিজ্যুয়ালাইজেশনে। এটা ঠিক, আমাদের প্রতিদিনের কাজে এই ঝামেলা অনেকটাই কমন। \n", + "\n", + "এ ব্যাপারে আমাদের মাথা অসাধারণ ভালো। ডাটার প্লটিং থেকে প্যাটার্ন বের করতে ওস্তাদ। খালি চোখেই অনেক কমপ্লিকেটেড ছবি থেকে এর ভেতরের মেসেজ নিয়ে আসতে পারি আমরা। পাইথনে \"ম্যাটপ্লটলিব\" হচ্ছে ভিজ্যুয়ালাইজেশনের ডিফ্যাক্টো স্ট্যান্ডার্ড, তবে স্ট্যাটিসটিকাল ভিজ্যুয়ালাইজেশন টুল হিসেবে \"সীবোর্ন\" মাথা খারাপ করে দেবার মতো। এটা আসেও স্ট্যান্ডার্ড সব মেশিন লার্নিং প্যাকেজের সাথে। আর দেরি কেন?" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "আইরিস ডেটাসেট এতোই জনপ্রিয় যে এর একটা ইনস্ট্যান্স দেয়া হয়েছে সিবোর্ণে। ব্যবহার করি sns.load_dataset('iris') মেথডকে। " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "iris = sns.load_dataset('iris')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "পৃথিবীর সবকিছুই আপেক্ষিক। একটার রেফারেন্সে আরেকটা আপেক্ষিক। ডেটা নিয়ে কাজ করতে গেলে প্রতিটা জিনিসের সাথে প্রতিটার একটা সম্পর্ক থাকে। আপনারা সামনে ডেটা নিয়ে কাজ করতে গেলে বুঝবেন কিভাবে সামান্য ‘কোরিলেশন’ একে অপরের সাথে জুড়ে আছে। সেটার হিটম্যাপ দেখি একটা। যেহেতু ডেটাসেট খুব বড় নয়, সেকারণে স্ট্যান্ডার্ড ‘কোরিলেশন’ কোএফিসিয়েন্ট বের করতে পারি প্রতিটা অ্যাট্রিবিউটের জোড়াদের সাথে। এটাকে আমরা বলি পিয়ারসন এর \"আর\"। সেকারণে corr() মেথড এর ব্যবহার। " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "correlation = iris.corr()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width\n", + "sepal_length 1.000000 -0.117570 0.871754 0.817941\n", + "sepal_width -0.117570 1.000000 -0.428440 -0.366126\n", + "petal_length 0.871754 -0.428440 1.000000 0.962865\n", + "petal_width 0.817941 -0.366126 0.962865 1.000000" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "correlation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "একটা মজার জিনিস দেখছি এখানে। স্ট্যান্ডার্ড ‘কোরিলেশন’ কোএফিসিয়েন্ট সাধারণতঃ -১ থেকে ১ হয়। মানে লিনিয়ার কোরিলেশন হচ্ছে যখন একটা বাড়ে, তখন আরেকটা জিনিস সেভাবে বাড়ে বা কমে। যখন এটা ১ এর কাছাকাছি হয় তখন সেটাকে একটা শক্ত পজিটিভ কোরিলেশন হিসেবে ধরা হয়। এদিকে সেটা যদি -১ এর দিকে যায়, তখন সেটাকে আমরা শক্ত নেগেটিভ ‘কোরিলেশন’ বলি। আবার ০ এর কাছে হলে এটার কোন লিনিয়ার ‘কোরিলেশন’ নেই। petal_length এর সাথে petal_width এর সম্পর্ক 0.962865 মানে ১ এর কাছাকাছি। sepal_length এর সাথে petal_length এর সম্পর্ক 0.871754 হলে শক্ত পজিটিভ কোরিলেশন আছে বলে ধরা হয়। বাকিদের সাথে অতো শক্ত পজিটিভ কোরিলেশন দেখছি না এমুহুর্তে। দেখি ছবি কি বলে? ঠিক ধরেছেন। একই জিনিস তবে খালি চোখে চট করে ধরা যায়। এজন্যই ভিজ্যুয়ালাইজেশন এতো জরুরি। ডান পাশের কালার ম্যাপ দেখে এক নজরেই কোরিলেশন বোঝা সম্ভব। " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(,)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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P+lHHsnHihw0Sc3064pBM3vlwNQBz5+fSqmUymRnNKtRr0TzCeWd2ZcyLy8qU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\nd/N933H+1V+TkhLhwP1blVk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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.heatmap(iris.corr(), cmap = 'coolwarm', annot=True)," + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ডেটার ভেতরে কি আছে সেটা জানতে শুরুতেই দরকার ডেটা ভিজ্যুয়ালাইজেশন। ডেটাকে ভালোভাবে ভিজ্যুয়ালাইজ করতে একটা ভালো উপায় হচ্ছে স্ক্যাটার প্লট। একটা স্ক্যাটার প্লটে একেকটা এক্সিসে (মানে x এক্সিসে একটা ফিচার এবং y এক্সিসে আরেকটা ফিচার) ধরে সেটাকে বিন্দু ধরে প্লট করলে চমৎকার ধারণা পাওয়া যায়। এখন কম্পিউটার স্ক্রিন দুই ডাইমেশনের হওয়ায় আমাদেরকে সন্তুষ্ট থাকতে হয় একেকবারে দুই/তিনটা করে ফিচার প্লট করে। তবে সেটার 'ওয়ার্কঅ্যারাউন্ড' আছে একটা। \n", + "\n", + "এবার একটু আমাদের তিনটা প্রজাতির ডেটার প্লট দেখি। খালি চোখে। আপনার বোঝার জন্য একটু \"বেস্ট ফিট লাইন\" ড্র করাতে বুঝে গেলেন কিভাবে একেকটা প্রজাতি আলাদা। এর মানে মেশিন লার্নিং ছাড়া নতুন ডেটাকে প্রেডিক্ট করতে পারবেন সহজে। এই ছবি দেখে। এখানে শুধুমাত্র petal_length এবং sepal_length\tব্যবহার করা হয়েছে দুই ডাইমেনশনের ছবি হবার কারণে। " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "sns.set(style=\"whitegrid\", color_codes=True) # change style" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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S9aqYnxEpVJxmHlSoKNctoiTC6rbCJlgh+nXKtbjM2HNlD/Z2NsEijPUiYsBg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\nYLej5eVhP3OW4ld/0/DyaYfD78wM9dGoWvpaTU1FKy9DK/QTJt270f6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.lmplot(x=\"sepal_length\", y=\"petal_length\", hue=\"species\", data = iris, \\\n", + " palette=\"Set1\", markers=[\"o\", \"s\", \"D\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "একই জিনিস, \"বেস্ট ফিট লাইন\" ছাড়া। মার্কারগুলো একটু দেখুন ভালো করে। " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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JeOmll5CWloa4uDjs2bMHDz30kNi1kcQYL8yt2VePiBzToE+fepmO7o5sCiqV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\nNNt3QDC+fCgIEPLzodm+Aw3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hQ1BZWYno6Gh0794dBw8eNPn69u3bY/To0YiJiYFarUbj\nxo0NZ159+/bF+PHj8a9//auOnwoRmcPu6SRpcXFxiI+PR/fu3V1dCs6fP499+/YZbksxceJEDBs2\nDP369XNtYUQejmdU5JHi4uJMTnSIjY2t872NWrVqhaysLAwcOBAymQy9evWq19vJE3krnlEREZGk\ncTIFERFJGoOKiIgkjUFFRESSxqAiIiJJY1AREZGkMaiIiEjS/h/LNRt7/iZJYwAAAABJRU5ErkJg\ngg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# sns.scatterplot(x = \"sepal_length\", y= \"sepal_width\", \\\n", + "# hue = \"species\", style = \"species\", data = iris)\n", + "\n", + "#sns.lmplot(x=\"sepal_length\", y=\"sepal_width\", data=iris, hue=\"species\", \\\n", + "# , legend=False)\n", + "\n", + "sns.lmplot(x=\"sepal_length\", y=\"petal_length\", hue=\"species\", data = iris, \\\n", + " palette=\"Set1\", fit_reg=False, markers=[\"o\", \"s\", \"D\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "আরো ফিচার দেখতে চাইলে? 'ওয়ার্কঅ্যারাউন্ড' হচ্ছে \"পেয়ার প্লট\" যেটা সবগুলো ফিচার ধরে ধরে প্লট করে জয়েন্ট রিলেশনশিপ আর হিস্টোগ্রাম দেখায়। আমাদের ডেটাসেটে চারটা ফিচার মানে ৪ x 4 = ১৬টা পেয়ার প্লট হবার কথা। শুরুতে দুটো দিয়ে sepal_length এবং petal_length পাশাপাশি। একেকটা ফিচার দুদিক থেকে। \"sepal_length\", \"petal_length\" ফিচার দুটো দেখে কী মনে হচ্ছে? এগুলো বেশ ডিস্টিংক্ট। বিশেষ করে 'সেটোসা' প্রজাতি বেশ আলাদা। অন্যদের সাথে মেলা দুস্কর। " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ArAm5sWi4TQoAAKA1MZM1kXbPDMuyJsi6QKwxNSuUPw3jLgAEZvPYcZ4/W3iu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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.pairplot(iris, size=5, vars=[\"sepal_length\", \"petal_length\"], \\\n", + " markers=[\"o\", \"s\", \"D\"], hue=\"species\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "এখন আসি একদম ১৬টা পেয়ার প্লটে। একেকটার সাথে আরেকটার পেয়ার প্লটিং। ঘুরিয়ে ফিরিয়ে। সব পারমুটেশন কম্বিনেশন করে। একেকটা ফিচারের গোপন খবর বের হয়ে আসছে। কার সাথে কার সম্পর্ক ভালো অথবা খারাপ? দেখুন মিলিয়ে। কী? কিছু মিল পাচ্ছেন কোরিলেশন আর হিটম্যাপে?" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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PA+jqL/LMDFTu2YeERx4CAgOBzv5E8poEH9bZshl7jQmX4Q85XXsm/nJ4HWvP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\nWvDoo49i37592kupiHtytjgmxBx0xsJBZWVlcSZWZ2ZmWnxbWo2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37oMJQdAQRLMOTRmH9SMmnXEvrLUytHXqaVD9qeAqkPBHiGlRzEgT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\nmBd4DjarAJfdYh53xTkzhw0kAH3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OH0Hwk4N0XyaEjCoKJmqY8cUCwPzvSF8ghZxTjvcg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EkJ04vIywAvqbkSLPh5nci364nq71wgQgghPRPzwcQvn/8Mbk/PF1HrL+Hng1HZ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\nsmHDBmRkZHT4fHNzM0pKS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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.boxplot(data = iris, orient = \"h\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "এই প্লট দেখলে ফিচারগুলোর অনুপাত নিয়ে একটা ধারণা হবে। " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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58vyBp5eT6Mgs4f6yFpO5c3Ua69vrrW93xIQJE7B+/Xr8+c9/RnR0NH73u9+h\nvLwcubm5AAC9Xo+zZ8+Kx1+8eBFRUVFiwNx+++1YuXIl8vPz8euvv+Ivf/kLgoODMX36dISFhaG+\nvh55eXmIjIyEVquFTqezWoc7OBROly9fxsSJEwEA8+bNk6wYIvIcbywnYWvmc1OmzyQ5Ep6I9s25\nPrt2UqG6Vm25Pb7j6ybt2LEDaWlpmDlzJj777DOsXLkSf/jDH7B06VIYDAYUFRUhKSkJCoUCBoMB\n1113HdRqNWpqapCQkIC9e/fixhtvxJ49e5CQkIB//vOfOHDgAFauXIlHHnkE1dXVePXVV1FfX49/\n//vfEATBlbfALofCKSgoCPfeey969uyJMJO1XDi3HpHvMTYVOBsU7nhNwPFZwh0NT8N9nl9ryB1G\npCe3uedkNDw9ucPnTE1NRX5+PtasWYOgoCCsXr0aW7duRU5ODrRaLUaMGIGoqCikpqbilVdeQUpK\nCv7+979j1qxZUCgUiI2NRUFBARQKBZ555hls2LABQUFBmDFjBm666SYUFRVh0qRJCA0NRffu3VFT\nU4Pu3bu78jbY5FA45efnS/LiFNjk1q0mJ7beG61W69JMFeZNBY4EhbPt5ebHW2tkcOSZJEfDs9LO\nyMnTrfHOMN5X2rH3NM7Va9A1XoXhLnbrJScn41//+lebbcZp6EwNGzYMw4YNE7/+/e9/b3HM22+/\nbbHNk9PWORRO6enp7R9E5ADTD13zVWyTYrp6uhyPcTaI520vsHh/gNb7v/O2F3QovG01FdgLCmfb\ny82Pt9fI4MgzSY6EZ2VZmUO1yNGgmxI80jruixwKJ6JAYO8Dv8zGB6CvaK+pwFpQONshZ358w5Gj\nUP9SYfM1jf/f3j2tjsz84I/dfYGG4UTk5xztyDMNCmc75MyP1zc24sLOUquTsJqfx5HLbs7M/OCv\n3X2BhuFE5MfMmwoMzc0WE6OaduQZg8naBKrGYwGIHXLayiqoKyosXsN0NVrAcnn3jnQBGsPTHk+3\nxpN07K7nRES+zdhUALSOZq7W1EDf2NjmGNOOPG1lFU6/t9HqcUZnS7bBcKEWNbtKcbRwOU6/t7HN\n/qDQUHFlWqA1oAzNzTZf0xnqigr8vGo1anaVWuxztLvPuK4TyZtkIyedTof58+fjzJkzaG5uxvTp\n0zF8+LWWz6+//hqvv/46goODMX78eEyaNAlNTU3Iz89HXV0dVCoVCgsLER8fL1WJJBE5TctDrSOF\nhiNHcWFn6we66WjG/BKZuuLaPSJbo57EB0bj15O/4uyhcgSFhsJw9Sr0jY1tjjP+2bi+kukorKMN\nCu3dR/JkazxJT7JwKikpQVzf60VOAAAgAElEQVRcHJYtW4aLFy9i3LhxYjjpdDoUFBRgy5YtiIiI\nQHZ2Nu655x589tln6Nu3L2bNmoVt27ahqKgICxculKpE8jLz7rxAbx839fJ9z1kNea1W6/S5anaV\nQv1LBUJiY9tcbosbNLDNh7vxw980WMwDShyF7dmL0EiVuE/f2Gg1oOIGDWzTFOGOYDKy9hCuo89Q\nkX27d+9GdXU1srKyHP6eNWvWoHPnzsjOznZLDZKF06hRozBy5Ejxa6VSKf65oqICycnJiP1t6J+W\nlob9+/ejrKwMf/7znwEAQ4cORVFRkVTlkZcEagB15Oe29j1lZWVOPedk+qFuPppR/1KBml2lFp15\n5scaAyr5ockArH/wWwuojraj2/sZzJ0t2YaWW/oDJu+Jr6zrJOfn/IYOHerV1wckDCeVqvVfVWq1\nGrNnz24zF59arUa0yb+wVCqVOLGgcbtKpUKjjWve5lxt85VTm7BcanGlDnv/unf2vHJ5PwD51OJo\nHYYLtdB9UtJ2ozIIQnQ0mpVBaNZqcHzjJvxaXQ196X8sT6AMghAeDmg1uHr1qsVxWq3G8njtFTQL\nBgT/4feojI5qfQYpOgqG+4Zf+9oJVn8Gc3v2Yl+3rm2Xjo+OQsst/aHfsxfBd6R36LWd4cqD0e40\nc+ZMTJkyBenp6fjxxx/x2muvoXPnzjh16hQMBgOefvpp3HHHHRgzZgxuvPFGhIaG4qGHHkJhYSGC\ng4MRExOD5cuXY/v27Thx4gSeffZZFBUV4auvvkJLSwuys7MxefJk/POf/8S2bdsQHByMwYMHW0zU\n8PLLL4u/p2PGjMHUqVMxb948XLp0CZcuXcKbb74pDk5skbRbr7q6GjNmzEBOTg7Gjh0rbo+KioJG\nc+0XW6PRIDo6us12jUaDmJgYh17HlV8MZ/8lKiW51OJqHZvrttvc58x55fJ+APKpxemRU3h4u/dg\nEoZloKZbN+vHRapgaI5B0oRxbY7TajWIjLScAy5x8iS3r6XU3s/QfEt/3D5qpOWOtDRoRwy3W4uc\nZ5DoiIkTJ+Ljjz9Geno6Pv74YwwZMgTnzp3DSy+9hIsXL+Lhhx/Gtm3boNVq8Ze//AU333wzCgsL\n8cc//hGPPvoovv76azQ0NIjnO3z4MHbv3o3NmzejubkZK1aswLFjx/C///u/2LhxI4KDgzFr1izs\n3LlT/J6dO3eiqqoKmzZtgl6vR05ODu68804AwJ133olHHnnEoZ9Fsm692tpaTJs2Dfn5+ZgwYUKb\nfSkpKTh16hQuXbqE5uZm7N+/H7fddhsGDRqE0tLWm7a7d++WxYcBkS8ydqQlDMsQ7xOZM59OyNZx\nxmBy9Hzu/rBv7zWVAyyn5zGyV0vNrlKbnX++asiQITh06BAuXbqE/fv34/jx49i9ezdyc3Mxe/Zs\n6PV6XLx4EQDQs2dPAMCTTz6J+vp6TJ06FV988QWCg6+NWU6ePIkBAwZAqVQiIiICCxcuxIkTJ3Dr\nrbciJCQECoUCgwcPxi+//CJ+T0VFBQYPHgyFQoGQkBDceuutqPit0cb4mo6QbOS0du1aNDQ0oKio\nSLx3NHHiRFy5cgVZWVmYN28eHn30UQiCgPHjx+P6669HdnY25s6di+zsbISEhHBBwwA2b3sBtFqt\n1VGYt6/HS8Gd9x8cmT/P2j0YZ4779eSvwKFyu8e5k73aOnK5zl9nkAgKCsKoUaOwZMkSjBgxAtdd\ndx26deuGJ598Ek1NTXjjjTfEy2lBQa1jk61bt2LcuHGYO3cu3nzzTWzatAmJiYkAgF69euGDDz6A\nwWBAS0sLHn/8ccydOxdvvfUW9Ho9lEol9u3bh8zMTBw9ehRA6+Djo48+wiOPPAKdTocDBw5g3Lhx\nAACFQuHwzyJZOC1cuNBup929996Le++9t822iIgIrF69WqqSyEHHq5uwo3h/h5eO9sfw8BWOzJ9n\nL0gcPU45IBWJPW/06Nx1zswSYY+/zyAxfvx4jBgxAl9++SUSEhKwcOFCPPzww1Cr1cjJyRFDyeiW\nW27BvHnzEBkZiZCQELzwwgvYt28fAKBfv34YMmQIsrOzYTAYkJ2djd/97ne4//77xW1paWkYMWKE\nGE733HMP9u7di6ysLOh0OowaNQr9+/d3+udQCFIuyOEBrt4LkMu9BEAetXx/rAZrt+yHKjKyzfbc\nP93s0QkqjSOnSLM6AO+En9R/N46OnOzVYaurzXwZCkcuu7V3nLEOb9yzMX9NZ/5u7HX+AZ7t6pNz\nt54ccPoiakOKpaNJeo5O2+NokLj7OHfq6Gt6Y3FFexhA9nH6ImpDiqWjSVqctscxplM52cIZJOSD\nIydqo2snFY5rLIPIlaWj3cW4vpG1yyGB/K9QTtvjOM4g4TsYTtTGiPRkHD9dY7HdlaWjHWW+EKHB\nYECQvvWZC39eiNAd+KHrOF+ZQSLQMZyojUE3JWD4rbE4fTHMbUtHd0RSTNc2DRG25przF46O/AwX\nam3u44eu49zV+UfSYTiRhd7dwpE1Rh4djHRNza5S6D4pQU14uMut4OTYKrye4m8zVbgDw4k8ztYI\nqKrhHC/f2eDMQ6Ny+tCVOzm8R65OjGuPM7OLX7hwAa+//jqWLFlidf+RI0ewY8cOzJw506012sJw\nIvIiR5516chDo3L40KX2ST1ThTOzi3fp0sVmMAGtD+T269fPDVU5huFEJGNcdtx/STFThfms5P/v\n//0/cSbx6dOnIy4uDkOHDsUdd9yB559/HiqVCp06dUJYWBhmzpyJvLw8bNq0CWPHjkV6ejqOHTsG\nhUKBoqIiHD58GBs3bsSqVauwefNmcVqj4cOHY9asWXj33Xexfft26PV6REdHY82aNQg1WWTSWQwn\n8hmB1i4ut4dGyX2k+keH+azkzzzzDM6da30E48KFC/jwww8RGhqKcePG4ZVXXkGfPn2watUqnD9/\nvs15NBoNRo8ejUWLFmHOnDnYvXs3OnduXZKkrq4O69evR0lJCUJDQ/Hyyy9DrVbj0qVLePvttxEU\nFIRHH30Uhw4dcmlWFYYTyUZSTFeHp+oJBHx+yT9J+Y+OIUOGYNmyZeKs5DfffLO4LykpSRzJ1NTU\noE+fPgBal7H5/PPPLc5l/N5u3brh6tWr4vbKykr06dMH4eHhAID58+cDAEJCQpCXl4fIyEicO3cO\ner3eqdrNcYYIIhlzdMkL8h1SzlRhPiu56QrkphO+du3aFcePHwcAHDx40Oq5bM0gnpycjBMnTqC5\nuRkAMHv2bOzduxdfffUVXn31VSxatAgGgwGuTtvKkRORzPH5Jf8j5UPTprOS79271+oxf/vb3zB/\n/nxxJvLrr7/e4fPHx8fjsccew8MPPwyFQoF77rkHt9xyCyIiIvDggw8iNDQUXbp0QU2N5cP8zmA4\nkce5696RHGd1lqom44fV8Y2bAjKY/PE5IKn+0dGtWzeUl7eutZWUdO0927Rpk/jnQ4cOYe3atYiP\nj8eqVasQEhKCpKQk8Zivv/5aPPbZZ58V/3zHHXcAAB588EE8+OCDbV73nXfecalucwwnIi9yJrAS\nhmXgVFNTwAWTlM8BeZu3Hpru1KkTpk2bhsjISERHR+Pll1/2yOs6g+FE5EOCunT2dgke5a8r1pry\nxkPTo0aNwqhRozz2eh3BcCK/U9VwzubltUBrR/dl/r5irSl/u2TpDuzWIyLZae85oJpdpR6uiDyN\n4UREsuKpxRMDffFFuWM4EZGseGLF2ppdpfh51WqOwGSM95zIZ9m6f+TNdZ94T8s9pHwOKBCaLPwB\nR05EJEvWZsdwZzAZ8R6WPDGciEi2TANKimAyYkDJDy/rkd/hpTX/4o7ngDjDu+9hOBF1gBynTvJn\nrgYGZ3j3PbysR0QBgTO8+xaGExEFDCmaLEgaDCciCijubLIg6fCeExEFHG9MtkrOYTgRdVBVwzmr\n2+dtL2BThA9gMMkbL+sREZHscORE1AEv3/ecV6dJIvJ3kobTwYMHsXz5chQXF4vbLly4gLy8PPHr\nI0eOYM6cOZg8eTKGDh2KG2+8EQAwcOBAzJkzR8ryiIhIpiQLp/Xr16OkpAQRERFttnfp0kUMqwMH\nDmDVqlWYNGkSTp8+jf79+2Pt2rVSlURERD5CsntOycnJWLNmjc39giBg6dKlWLJkCZRKJcrLy3H+\n/Hnk5ubisccew4kTJ6QqjYiIZE4hCIIg1cmrqqqQl5eHTZs2WezbsWMHtm/fjsLCQgDAvn37UFtb\ni/vvvx/79+9HQUEBPvzww3Zfo6yszO11EzliQ+UnNvdN7Z7pwUrI29LS0rxdgt/xWkNESUkJpkyZ\nIn6dmpoKpVIJABg8eDDOnz8PQRCgUCjaPZcrvxhlZWWy+cWSSy2sw5K1WrxRm1zeE7nUAcirFnIf\nr7WSl5eXY9CgQeLXr732GjZs2AAAOHr0KBITEx0KJiIi8j8eGzlt3boVWq0WWVlZqK+vh0qlahM+\njz/+OPLz81FaWgqlUomCArbpEhEFKknDKSkpSbzfNHbsWHF7fHw8Pv300zbHxsbGYt26dVKWQ0RE\nPoIzRBARkewwnIiISHYYTkREJDsMJyIikh2GExERyQ5nJfcB3x+rwVd7T+NcnQZdO6kwIj0Zg25K\n8HZZRESSYTjJ3PfHalD8+WHx6+patfg1A4qI/BUv68ncV3tPW92+w8Z2IiJ/wHCSuXN1Guvb661v\nJyLyBwwnmevaSWV9e7z17URE/iDg7zkdr27CjuL9sm02GJGe3Oaek9Hw9GQvVENE5BkBHU7fH6vB\njoOXoYqMBCDPZgNjHTv2nsa5eg26xqswXGYBSkTkbgEdTvaaDeT04T/opgRZ1UNEJLWAvufEZgMi\nInkK6HBiswERkTwFdDiNsNFUwGYDIiLvCuh7ToNuSsDwW2Nx+mKYW5oN/H2aIX//+YhIPgI6nACg\nd7dwZI1Jc/k8/j7NkL//fEQkLwEfTu7iSuefcURScboGKYf3d3hEIuXIxlc6G4nIPzCcOsg8CE6c\nuYSIMMu3s73OP9MRiYCOj0jsjWwAuBxa7GwkIk9iOHWAtSC4rG6GIACR4W3f0vY6/9w1IrF1ns1f\n/YymZr34dUXVRRw4VoPYqFD0uiHO4aDq2kmF6lq15XZ2NhKRBAK6W6+jrAVBjCoEDZpmi+3tdf65\na0Ri6zynzjWIf9Y26VB3+Sp0egMuq5vF0dX3x2raPT87G4nIkxhOHWAtCCLDQxAbFYrEzlEIClIg\nsXMUcv90c7ujEnc9a2XrPEbaJh1qLl5Bs74FOr0BV3Ut4j5Hlt8YdFMCcv90s9M/HxFRR/CyXgfY\nusSVckMc8nMHO3Uud03saus8PbrGoL7hCuouX0WLQQAACBDQ0gJom/SIDA92eJTGaZSIyFMYTh3g\nzpnCTSd2rajUIrFzlHieV5ycLT08NBi/nmuAAkCPbjGYOLwvAGBZ8X4AgAIKCGgNKKVSgQZNMyLD\ng3nfiIhkh+HUAe6eKdw4IikrK0NaWprTzxSZHt81vnWG9aarevH42KhQXFY3wyC0jpiUSgWCFAro\nWgwAeN+IiOSH4dRBUl7icraDr73je90QJ16G1Dbp0KDRQac3ICYylPeNiEiWGE4y5EgHn+lzVtV1\nGsREhlq0sRuPN70MGRkegsjwEABgMBGRbDGcZKi9Z4rML/tBAOouNwEIbxNQxuO5YCER+RqGk5M8\nMflpew0X5pfxYlQhqLt8VWxwMD8ekL7TjpPCEpE7MZyc4KnJT9sb6Zhf9jNepmvU6hAUpPD4yIiT\nwhKRuzGcnODJyU/tjXSsXfaLDA9B76TrnH7Oyh04KSwRuZuk4XTw4EEsX74cxcXFbba/9dZb2LJl\nC+Lj4wEAzz//PBITE5Gfn4+6ujqoVCoUFhaK++XCXVMNuXoJzJ3PWVmrp8d1V+HMIiKcFJaI3E2y\ncFq/fj1KSkoQERFhsa+8vByFhYVITU0Vt7311lvo27cvZs2ahW3btqGoqAgLFy6UqrwOccfkp+3N\nHu4IdzY4WKvn+Gkt+vSpcfh8nBSWiNxNsrn1kpOTsWbNGqv7ysvLsW7dOmRnZ+PNN98EAJSVlWHI\nkCEAgKFDh+Lbb7+VqjSbvj9Wg1eK9yPv1VK8UrzfYkJUd0x+au8SmDN1frX3NKrrNLjexftL7qiH\nk8ISkbtJNnIaOXIkqqqqrO4bPXo0cnJyEBUVhZkzZ2Lnzp1Qq9WIjo4GAKhUKjQ2Njr8WmVlZS7V\nWlZWhuPVTdhx8LK47bhGg+OnazD81lj07hYubr+zTwh+OKHBRbUe10UFY2AvFQR1JcrKKh16rYrT\nNb9NIGS2vVKLe2++vt2fxdE6HWW7nhqn3ldX3xdbXP27dSe51MI6LHm7lrQ011fTprY83hAhCAKm\nTp0qBlFGRgYOHz6MqKgoaDSt9yg0Gg1iYmIcPqcrvxjGKYN2FO+HKjLSYv/pi2HIGpNmcl9Gj66d\n4jFxZMdGKymH91u9BJbYOQqA/Z/l+2M1+OLAD2jUGBASHIQY1bUHb411uqMejVaLlO4JTr2vaQCy\nnH51+4x/N3Igl1pYhyU51ULu4/ElM9RqNcaMGQONRgNBELBnzx6kpqZi0KBBKC0tBQDs3r3b479s\nJ85cwrk6LSrPq3GuTgttU+vcdOfqNeJ9mepaNQRBcGodJHMdvQRmrKHxtzWjdHoD6i43ob6hCefq\ntNhz+JzVS5HtnfNiQ5PFz+xIPUREUvLYyGnr1q3QarXIysrCM888gylTpiA0NBR33XUXMjIykJ6e\njrlz5yI7OxshISFYsWKFp0rD98dqcFndDJ2+dSJU4wc/EI7eSXF2V5l1tuvOXjODvUtgxhpCgoPE\nOg0GAZcamxESHISQ4CCnni8ybYToFBuGBo0OdZebEB8bhzv7xLIFnIi8StJwSkpKwqZNmwAAY8eO\nFbdnZmYiMzOzzbERERFYvXq1lOW0YbxMV3G6Bs2GOoSGXPvQN2rQNGN4ejLe/d8jFt+vbdKhskaN\n7gmtl+McCQbzlu2HRvVzOASM7drG2SAAoMUgiEtgxKhCxWMdeb7INHBN59uLjw5H724Kh2oiIpJK\nQD6EazpqEADxUpkqIhjNOgN0+tZ7OrFRoRh0U0JrZ5zZfZkGjQ4hSsurosZgMA+iGxNj8O2PZ8Xj\nnJ1FwdiubQyRBo0OzfoWKIMU6BTbdk49R54vsv9sUlS7309EJKWAXKbd/DJdSHDr29CsM6Brp0h0\nvz4KXTtFIuWGOADW7xPp9IY2oxUjW/eoPt55vM09HSNHW7ZNa4gMD0HXTpFQhYcg4bpIi9nIHXm+\nyF3LwxMRSSEgR07mowbjpTLj4ntGxqYAa/eJwkOD0dRsGTahwUqs2fQDGjXNbTrqdHqDxcSsgOOz\nKFir4a4BiW1GY+Z122NvlglB7Vj7Nyd7JSKpBGQ4mc9oYLxUptMLNidONZ/rzmLZCrTeh9I26S06\n6oDw1kYGs/ADnBupWJtvr3dSXIdmirAWdj0SY8T7cCmH94ujNWsBZPrza5t0OHBMg73l59Cnexwm\njujLkCIilwRkOFkbNUSGhzi1+J61D/f6hiY0NevRoGnbXNGgaUaMKgQNGp3FeVxt2XZlKQzT7zW/\nD1ddq8abHx0EoBBHe6b3yYyXRrVNOrFBAwB+rW7gjORE5LKADCfTYKmo1CKxc1SHpgAyD4a8V1uf\n0zLtqAMAXYsBkeEhGJ7eA6fONshywT9r7fLGMDW/FLnjt5GU6TFGxtEhZyQnIlcEZDgB14LFHU+X\ni3Pd1RrbvUPFZ4d0egNiIkM7tCS6J+/pWOve0+kNgJWu8nP1GvHSqHn7vbGDkTOSE5ErArJbz51M\nO/NiVCEm95kUYuffzEkDOxRM7pqVwhHWuvdCgoOstst3jVeJ96OMnY5Gxg5Gdv0RkSsYTi4yf5i1\nU2wYQoKD0KhtRmLnqA6NmMzPa8qZ2cKdYa1dPkYVYrVd3ng5MvdPN+PGbjGAojWkTJ+34vRHROSK\ngL2s5y7WlkyPDA9BUJDCpVVpPb2An637cMZt1u6TGS+Nfn+sxi1rSxERGTGcrBCnNjpzCc06A8JC\ngtDrhjir93wcXWjP2ftH3ljAz9Z9OEfmC2QYEZE7MZzMGO/1mLZINwIQhEtiWJh+EDuyZLq91W9N\nW7k3f1OH9/5TKk53ZC2ceLmMiAIBw8mM8V6PeYu0cXYH8xZpR5ZMt3f/yPSBVo1WD1VkKKpr1aiu\nVeOuAYleaz3n7A9E5E0MJzPGez3mLdLG53es3fNp77JWe/ePbIXXqbMNLt236qjj1U347hf7Iz0i\nIikxnMwY7/WYrpsEXHt+pyP3fNq7f+Tp5gfA/sjoQIUGgGWXHh+sJSJPYSu5GWNLdYyqdb49g0GA\nTm/AVV0LztVp0SPR8eXjzc9pznj/yNMzhLf3DNVFteWEtgAfrCUiz2E4mTE+v9M76TpERQZDgACl\nUoGwECViVKH49sezTj8IazxnYucoBAUpLJ5/6ujS7R3V3jNU10VZH1DzwVoi8hRe1rPCeA/pleL9\nVi/HdeTylrX7UqaX1sJDg6FrVticFd2d2ruMeFuKCt/90rYhRNukQ31DE/JeLUVYiBIAcFXXwmYJ\nIpIEw8kOKe8FmbeXNzXr0awXMM2Jpds7qr17YL27haNPnz5iB2JosBLaJj2amvXQNulw+lxri32n\n2HA2SxCRJHhZzw4p7wV5enoiU45cRhx0UwLycwdjxVMZuC7m2rREpi32Db+tWwV4pm4iChwMJzuk\nvBfkjQ49o/bugVnUZFKraQej6eKJbJYgInfiZT07HHnAtqO8MT2RKWemHDKt1bTF3nTGcjZLEJE7\nMZzaIdW8cY5Me+Qqd83yYFqr6UKKpjOWc1olInInhpOXWBuVJV8X4rYgdGQ+v47WGh8TASiAZl0L\nZyEnIkkwnLzIfFRWVlbmtnO3N5+fszjzOBF5EsNJYt6aQNWbDRdERK5iOEnInZfWnOXthgsiIlcw\nnCTUkUtrUjQxmGLjAhH5AoaThJy9tObOpSqkbIMnIpIaw0lCzl5ac9dSFeajr4c8MCUSEZE7cYYI\nCTk7w4Q7lqpobzkMIiJfwJGThJy9tHZdVDCuGiy3O9PE4O4WciIib5A0nA4ePIjly5ejuLi4zfbP\nPvsMGzZsgFKpRN++fbFkyRIEBQUhMzMT0dHRAICkpCQUFBRIWZ5HOPN8kLWlKgDnmhjYQk5E/kCy\ncFq/fj1KSkoQERHRZntTUxNeffVVbN26FREREcjLy8POnTtx9913A4BFkAUS86UqOtLEwBZyIvIH\nkoVTcnIy1qxZg7/+9a9ttoeGhmLjxo1iaOn1eoSFheHo0aO4cuUKpk2bBr1ej7y8PAwcOFCq8mTL\n1ZkY2EJORP5AIQiCINXJq6qqkJeXh02bNlndX1xcjNLSUqxfvx4///wzDh48iIkTJ+LXX3/FY489\nhi+++ALBwfbz051T/viL49VN+OGEBhfVelwXFYyBvVTo3S3c22UR+a20tDRvl+B3vNIQYTAYsGzZ\nMpw8eRJr1qyBQqFAz5490aNHD/HPcXFxuHDhArp169bu+Vz5xSgrK5PNL5a7akkDkCWDOlwllzoA\n+dTCOizJqRZyH6+0ki9evBhXr15FUVGReHlvy5YtePnllwEA58+fh1qtRpcuXbxRHhEReZnHRk5b\nt26FVqtFamoqtmzZgsGDB2Pq1KkAgClTpmDChAl47rnnkJ2dDYVCgZdeeqndS3pEROSfJP30T0pK\nEu83jR07Vtx+9OhRq8evWLFCynKIiMhHcIYIIiKSHYYTERHJDsOJiIhkh+FERESyw3AiIiLZYTgR\nEZHsSDp9kSdw+iIikgPOUuFePh9ORETkf3hZj4iIZIfhREREssNwIiIi2WE4ERGR7DCciIhIdgI2\nnAwGAxYvXoysrCzk5ubi1KlTHnttnU6H/Px85OTkYMKECdixYwdOnTqF7Oxs5OTk4G9/+xsMBoPH\n6qmrq0NGRgYqKiq8Wsebb76JrKwsPPjgg9i8ebNXatHpdJgzZw4mT56MnJwcr70nBw8eRG5uLgDY\nfP3XXnsNEyZMwOTJk/Hjjz9KXseRI0eQk5OD3NxcPProo6itrQUAbNq0CQ8++CAmTZqEnTt3SlKH\neS1GW7duRVbWtaU1PVULeYAQoL788kth7ty5giAIwoEDB4Qnn3zSY6+9ZcsW4e9//7sgCIJQX18v\nZGRkCE888YTw3XffCYIgCIsWLRK2b9/ukVqam5uFv/zlL8J9990nHD9+3Gt1fPfdd8ITTzwhtLS0\nCGq1Wli9erVXavn3v/8tzJ49WxAEQfjmm2+EmTNneryOdevWCWPGjBEmTpwoCIJg9fV/+uknITc3\nVzAYDMKZM2eEBx98UPI6HnroIeHw4cOCIAjCBx98ILz00ktCTU2NMGbMGOHq1atCQ0OD+GepaxEE\nQTh8+LAwZcoUcZunaiHPCNiRU1lZGYYMGQIAGDhwIH766SePvfaoUaPw1FNPiV8rlUqUl5cjPT0d\nADB06FD897//9UgthYWFmDx5MhISEgDAa3V888036Nu3L2bMmIEnn3wSw4YN80otPXv2REtLCwwG\nA9RqNYKDgz1eR3JyMtasWSN+be31y8rKcPfdd0OhUCAxMREtLS2or6+XtI6VK1eiX79+AICWlhaE\nhYXhxx9/xG233YbQ0FBER0cjOTnZ5npt7qzl4sWLWL58OebPny9u81Qt5BkBG05qtRpRUVHi10ql\nEnq93iOvrVKpEBUVBbVajdmzZ+Ppp5+GIAhQKBTi/sbGRsnr+OijjxAfHy+GNACv1AG0ftj89NNP\n+Mc//oHnn38ezz77rFdqiYyMxJkzZ3D//fdj0aJFyM3N9XgdI0eObLMKtLXXN//9laIu8zqM/4D5\n/vvv8e677+KRRx6BWq1GdHR0mzrUarVb6zCvpaWlBQsWLMD8+fOhUqnEYzxVC3lGwK6DHhUVBY1G\nI35tMBg8uix8dXU1ZlQTL1AAAANiSURBVMyYgZycHIwdOxbLli0T92k0GsTExEhew4cffgiFQoFv\nv/0WR44cwdy5c9v869tTdQBAXFwcevXqhdDQUPTq1QthYWE4d+6cx2t5++23cffdd2POnDmorq7G\n1KlTodPpPF6HqaCga/+GNL6++e+vRqNp88Eslc8//xxvvPEG1q1bh/j4eK/UUV5ejlOnTmHJkiW4\nevUqjh8/jhdffBF33nmnV94TkkbAjpwGDRqE3bt3AwB++OEH9O3b12OvXVtbi2nTpiE/Px8TJkwA\nANx8883Ys2cPAGD37t0YPHiw5HW89957ePfdd1FcXIx+/fqhsLAQQ4cO9XgdQOu8ZP/5z38gCALO\nnz+PK1eu4K677vJ4LTExMeIHWmxsLPR6vVf+bkxZe/1Bgwbhm2++gcFgwNmzZ2EwGBAfHy9pHZ9+\n+qn4+9K9e3cAwIABA1BWVoarV6+isbERFRUVkv+3NGDAAGzbtg3FxcVYuXIlevfujQULFnilFpJO\nwI6c/vjHP+L//u//MHnyZAiCgJdeesljr7127Vo0NDSgqKgIRUVFAIAFCxbg73//O1auXIlevXph\n5MiRHqvH1Ny5c7Fo0SKP13HPPfdg3759mDBhAgRBwOLFi5GUlOTxWh555BHMnz8fOTk50Ol0eOaZ\nZ5CamuqV98TI2t+JUqnE4MGDkZWVJXaeSqmlpQUvvvgiunXrhlmzZgEAbr/9dsyePRu5ubnIycmB\nIAh45plnEBYWJmkttnTp0kU2tZDrOPErERHJTsBe1iMiIvliOBERkewwnIiISHYYTkREJDsMJyIi\nkp2AbSWnwHTTTTfh2LFjqKqqwqhRo5CSkgIAaGpqwqBBgzBnzhx07tzZy1USEUdOFLASEhLw6aef\n4tNPP8UXX3yBzp07Y/bs2d4ui4jAcCICACgUCsyaNQu//PILJwslkgGGE9FvQkND0aNHD5w4ccLb\npRAFPIYTkQmFQoHw8HBvl0EU8BhORL9pbm7GyZMn0bt3b2+XQhTwGE5EaF0yZc2aNbj11luRnJzs\n7XKIAh5bySlg1dTU4H/+538AtIZTv379sHLlSi9XRUQAZyUnIiIZ4mU9IiKSHYYTERHJDsOJiIhk\nh+FERESyw3AiIiLZYTgREZHsMJyIiEh2GE5ERCQ7/x8bYMVXm7WxgQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "iris[\"ID\"] = iris.index\n", + "iris[\"ratio\"] = iris[\"sepal_length\"]/iris[\"sepal_width\"]\n", + "sns.lmplot(x=\"ID\", y=\"ratio\", data=iris, \\\n", + " hue=\"species\", markers=[\"o\", \"s\", \"D\"], fit_reg=False, legend=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "স্ট্রিপ-প্লট" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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SCaZMmYJTp04hICCgym2mpKTo+bYIMbyKigqttkKlRHJyMp0pZwTKn5To9KlVKt6NMVUW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\nHZKSksBxHMaOHYvOnTvjwIE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zszNmzZqF33//3SxFEpnLtO7jMbrDMJTpygARUCtUnC1DVumeDzGdOHECGRkZ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\nciXatGmDTz/9FOnp6UbnKSo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QDXT0iiC15PRLf3eDK939Yvk9ZYdiv75BPVhzcpNiyKO/kzcL9n5DjamWkR0G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\n0I2r+oaxemeq7cWoXqchu6Cc+99YjZP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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.swarmplot(x=\"species\", y=\"petal_length\", data=iris)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/kaggle/Iris_Species/test-scikit-learn.ipynb b/kaggle/Iris_Species/test-scikit-learn.ipynb new file mode 100644 index 0000000..e3d1029 --- /dev/null +++ b/kaggle/Iris_Species/test-scikit-learn.ipynb @@ -0,0 +1,1568 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + }, + "colab": { + "name": "test-scikit-learn.complete.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sTsUF1x2r2Pt", + "colab_type": "text" + }, + "source": [ + "## সাইকিট-লার্ন এর ডাটা লে-আউট, ডাটা হ্যান্ডলিং\n", + "রিভিশন ৫" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "J8tU0g0Wr2Px", + "colab_type": "text" + }, + "source": [ + "### কম্পিউটারের ডাটা রাখার ধারণা " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "shSXhvSbr2Pz", + "colab_type": "text" + }, + "source": [ + "(না পড়লেও চলবে)\n", + "\n", + "আমরা তো এটা বুঝে গেছি যে মেশিন লার্নিং মডেল তৈরি করে ডাটা থেকে। ভালো কথা। তো, ডাটা এক্সেস করবো কিভাবে? আর, তাই কম্পিউটার কিভাবে ডাটা রাখে সেটা নিয়ে কিছুটা আলাপ করা যায় বরং। তবে, সেটার স্কোপ কমিয়ে আনার জন্য আমার প্রস্তাব হচ্ছে, \"সাইকিট-লার্ন\" কিভাবে ডাটা রাখে সেটা বোঝা দরকার। রেডি তো?\n", + "\n", + "আমার ‘মেশিন লার্নিং’ এর হাতে খড়ি হয় ‘আর’ প্রোগ্রামিং এনভারমেন্ট দিয়ে। একটা অসাধারণ এনভায়রনমেন্ট বটে। আপনারা সবাই জানেন যে ‘আর’ এর কাজ শুরু হয় পরিসংখ্যান এর ধারণা থেকে। আজকে ‘মেশিন লার্নিং’ এর যত ধারণা তার বেশিরভাগ মানে প্রায় সবকিছুই এসেছে এই পরিসংখ্যান থেকে। বলতে পারেন কম্পিউটারের ‘প্রসেসিং পাওয়ার’ এবং 'ডাটা স্টোরেজে'র দাম কমাতে অনেক ডাটা অল্প খরচে প্রসেসিং করার সুবিধা পেল মানুষ। সেই সাথে বুঝতে শুরু করেছে ডাটা কিভাবে আমাদের জীবনকে পাল্টাচ্ছে। \n", + "\n", + "কম্পিউটার ডাটা রাখে নিচের ছবির মতো করে। মানে একেবারে ইউনিট লেভেলে। মনে আছে ভেক্টর, ম্যাট্রিক্স, অ্যারে, ডাটাফ্রেম, পাইথনের লিস্ট এর কথা? এরাই ডাটা রাখে --- কখনো বিভিন্ন সারি আর কলাম নিয়ে। আবার কয়েক ডাইমেনশন নিয়ে। আচ্ছা, এক ধরণের জিনিস তো এক জায়গায় রাখা যায় তবে কি হতে পারে যখন বিভিন্ন জিনিস রাখবো এক টেবিলে?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "crVnCRNSr2P0", + "colab_type": "text" + }, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sfKN5MUCr2P1", + "colab_type": "text" + }, + "source": [ + "সারি ধরে ডাটা রাখার সবচেয়ে ছোট ইউনিট ধরতে পারি এখানে ভেক্টরকে। একটা ভেক্টর হচ্ছে এক ডাইমেনশনের একটা কালেকশন - যেটা হতে পারে লিস্ট, সেট, নামপাই অ্যারে (numpy.array) অথবা পান্ডাজ সিরিজ (pandas.series) - নিচের ছবি দেখুন। আবার, নামপাই এর একটা অ্যারে কয়েক ডাইমেনশনের হতে পারে। একারণে একে আমরা বলি 'এনডি' অ্যারে। মানে “এন” সংখ্যক অ্যারে। এই কনটেইনারে একই টাইপ আর সাইজের জিনিস থাকবে। আর ম্যাট্রিক্স হচ্ছে দুই ডাইমেনশনের একটা কনটেইনার, যেখানে সারি, কলাম সহ একটা নেস্টেড লিস্ট বা নামপাই অ্যারে (numpy.array) অথবা পান্ডাজ ডাটাফ্রেম (pandas.DataFrame) থাকতে পারে। \n", + "\n", + "তবে ডাটা সায়েন্টিস্টরা ভালোবাসেন ডাটাফ্রেম। সত্যি বলতে - বিভিন্ন ধরনের ডাটাকে এক জায়গায় রাখার জন্য চমৎকার জিনিস হচ্ছে ‘ডাটাফ্রেম’। মনে আছে এক্সেল এর কথা? এক্সেলের টেবিলটাকে আমরা “আর প্রোগ্রামিং” এনভারমেন্টে “ডাটাফ্রেম” বলি। আর এই ডাটাফ্রেম নিয়ে কাজ করতে করতে এর সুবিধা চলে এসেছে বাকি সব প্লাটফর্মে। ডাটাফ্রেম হচ্ছে দুই ডাইমেনশনের বিভিন্ন রকম জিনিসপত্র রাখার অ্যারে। আগেই বলেছি জিনিসটা দেখতে একেবারে আমাদের এক্সেলশিটের মতো। এই ডাটাফ্রেম নিয়ে কাজ করার জন্য পাইথনে আমরা ব্যবহার করি ‘পান্ডাজ’। ডাটাফ্রেমে আমাদের দরকারি ডাটা স্ট্রাকচারে ডাটা ‘ম্যানুপুলেশন’ খুবই সোজা। সত্যি বলতে ‘আর’ প্রোগ্রামিং এনভারমেন্ট এর সব সুবিধা নিয়ে এসেছে এই পান্ডাজ। আমাদের ডাটাফ্রেমে তিনটা আসল কম্পোনেন্ট থাকে। ১. ডাটা ২. ইনডেক্স ৩. কিছু কলাম। একটা ডাটাফ্রেমে, ডাটা হিসেবে নিচের কয়েকটা জিনিস থাকে।\n", + "\n", + "১. শুরুতেই পাণ্ডাজের ডাটাফ্রেম। সেটা তো অবশ্যই। \n", + "\n", + "২. পাণ্ডাজের সিরিজ। এটা একটা এক ডাইমেনশনের লেবেলসহ অ্যারে, সঙ্গে থাকছে অ্যাক্সিস এর লেবেল বা ইন্ডেক্স। সোজা কথায়, একটা সিরিজ অবজেক্ট হচ্ছে ডাটাফ্রেমের একটা কলাম। বোঝা গেছে তো?\n", + "\n", + "৩. 'নামপাই' 'এনডি' অ্যারে। আমরা এটাকে রেকর্ড বলতে পারি। \n", + "\n", + "৪. দুই ডাইমেনশনের অ্যারে। আগেই বলেছি - ‘এনডি’ অ্যারে হচ্ছে ‘এন’ সংখ্যক অ্যারে।\n", + "\n", + "৫. ডিকশনারি অথবা এক ডাইমেনশনের ‘এনডি’ অ্যারে, লিস্ট অথবা ডিকশনারি অথবা সিরিজ।\n", + "\n", + "আমরা এখানে একটা ছবি দেই বরং। " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JR41kfIOr2P5", + "colab_type": "text" + }, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "M54oqqBBr2P6", + "colab_type": "text" + }, + "source": [ + "এখানে একটা \"নামপাই অ্যারে\" তৈরি করলাম। আবার সেই \"অ্যারে\"কে ঢুকিয়ে দিলাম ডাটাফ্রেমে। " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9wTzqUler2P7", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import numpy as np # ইমপোর্ট করার ব্যাপারটা একটু পরে বুঝবো \n", + "data = np.array([['','Col1','Col2'],\n", + " ['Row1',1,2],\n", + " ['Row2',3,4]])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "hZR9uEicr2QC", + "colab_type": "code", + "colab": {}, + "outputId": "156b35c7-842e-4960-ff59-01ccc35719e8" + }, + "source": [ + "data" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([['', 'Col1', 'Col2'],\n", + " ['Row1', '1', '2'],\n", + " ['Row2', '3', '4']],\n", + " dtype=' এখানে ডাটাসেট আর তার এট্রিবিউট থাকছে \n", + "iris = datasets.load_iris()\n", + "# iris = load_iris()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nYjuOAJOr2Qy", + "colab_type": "text" + }, + "source": [ + "আমরা load_iris() ফাংশন দিয়ে iris নাম দিয়ে যেই অবজেক্টকে ফিরে পাবো সেটা আসলে সাইকিট-লার্ন এর একটা \"বাঞ্চ\" অবজেক্ট। জিনিসটা আসলে একটা ডিকশনারির মতো। ভেতরে কয়েকটা এলিমেন্ট আছে। কী, ইনডেক্স সহ। ভালো দিক হচ্ছে, সেটা তার বিভিন্ন এট্রিবিউটকে এক্সেস করতে পান্ডাজের মতো ডট নোটেশন (.) সাপোর্ট করে। কী-গুলোতে কোন স্পেস ব্যবহার করা যাবে না। দেখুন, ভেতরে iris.keys(), মানে এর ডাটা বা টার্গেটকে এক্সেস করতে গেলে iris.data বা iris.target ধরে ডাকতে হবে। " + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": true, + "id": "buthei-lr2Qz", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# এটা কিন্তু ডাটাফ্রেম নয়, বাঞ্চ অবজেক্ট, ডিকশনারি গোত্রের \n", + "# type(iris)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "m5TdujdAr2Q2", + "colab_type": "code", + "colab": {}, + "outputId": "010b8d03-354d-451b-c7fc-6f50d7140e3e" + }, + "source": [ + "type(datasets.load_iris())" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "sklearn.utils.Bunch" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 28 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "z8RYm9m6r2Q8", + "colab_type": "text" + }, + "source": [ + "এতো আলাপ করলাম, এখন বলুনতো আমাদের ডাটা টাইপ কী? নামপি এনডি অ্যারে। " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "yYN5EN_Lr2Q9", + "colab_type": "code", + "colab": {}, + "outputId": "dcc2a17d-73f8-4935-bc9f-40623822d3ae" + }, + "source": [ + "print(\"Type of data:\", type(iris['data']))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Type of data: \n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "U06rj1hor2RB", + "colab_type": "text" + }, + "source": [ + "### কিছু মেশিন লার্নিং টার্মিনোলজি \n", + "\n", + "১. প্রতিটা সারি হচ্ছে একটা অবজারভেশন (যাকে আমরা বলি স্যাম্পল, ইনস্ট্যান্স, রেকর্ড, উদাহরণ ইত্যাদি)\n", + "\n", + "২. প্রতিটা কলাম হচ্ছে একটা ফিচার (যার অন্যান্য নাম হচ্ছে প্রেডিক্টর, অ্যাট্রিবিউট, ইনডিপেনডেন্ট ভ্যারিয়েবল, ইনপুট, রিগ্রেসর, কোভ্যারিয়েট)\n", + "\n", + "৩. প্রতিটা ভ্যালু আমরা যাকে প্রেডিক্ট করবো, সেটার নাম হচ্ছে টার্গেট/রেসপন্স (এর অন্য অনেক নামের মধ্যে আউটকাম, লেবেল, ডিপেনডেন্ট ভ্যারিয়েবল ..)\n", + "\n", + "৪. আমাদের এই সুপারভাইজ্ড লার্নিং এর আউটকাম যেহেতু \"ক্লাসিফিকেশন\" এর মানে হচ্ছে আমাদের \"রেসপন্স\" হচ্ছে \"ক্যাটেগরিক্যাল\"। \n", + "\n", + "৫. যদি আমাদের এই সুপারভাইজ্ড লার্নিং এর রেসপন্স কন্টিনিউয়াস সংখ্যা হতো, সেটাকে আমরা বলতাম \"রিগ্রেসন\"। সামনে কথা হবে এটা নিয়ে। " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "alYf4nZZr2RD", + "colab_type": "text" + }, + "source": [ + "### এই অবজেক্টের ভেতরে কী আছে?\n", + "\n", + "আমরা দেখতে চাইবো আমাদের এই iris অবজেক্টের ভেতরে কি আছে? যেহেতু এটা একটা ডিকশনারি অবজেক্টের মতো, তার একটা ইনডেক্স আছে keys() দিয়ে এক্সেস করার জন্য। এখানে সবচেয়ে বেশি প্রয়োজনীয় জিনিস হচ্ছে 'data' আর 'target' যাকে এক্সেস করবো iris.data এবং iris.target নামে। কাজের শুরু অল্প দিয়ে। ঠিক ধরেছেন। এগুলো ডাটাফ্রেম নয়, বরং দুটোই অ্যারে। " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "r4We20cVr2RE", + "colab_type": "code", + "colab": {}, + "outputId": "2064852f-d677-4be8-c6a3-a96a93a6f5c1" + }, + "source": [ + "print(iris.keys())" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6rESmmE_r2RH", + "colab_type": "code", + "colab": {}, + "outputId": "1f2d73b7-aa12-4c97-b10b-f990aca08bc7" + }, + "source": [ + "dir(iris) # এটা একটা বিল্ট-ইন পাইথন ফাংশন, প্রায় একই কাজ করে " + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['DESCR', 'data', 'feature_names', 'target', 'target_names']" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 31 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c4zpiv5cr2RN", + "colab_type": "text" + }, + "source": [ + "শুরু করি গল্প পড়ে। আইরিস ডাটাসেট একনজরে। আইরিস ডাটাসেট নিয়ে একটা ডেসক্রিপশন ('DESCR') দেয়া আছে ডাটাসেট মেইনটেইনারের পক্ষ থেকে। না পড়লে বিপদে পড়বেন সামনে। অন্য কিছু না পড়লেও \"Data Set Characteristics\" এবং \"Summary Statistics\" পড়ে নেয়া জরুরি। print ব্যবহার করছি দেখার সুবিধার্থে। কি বুঝলেন? ভালো খবর হচ্ছে কোন ডাটা মিসিং নেই। এতো শান্তি কোথায় রাখবো! না হলে ওই ডাটা তৈরি করতে হতো টাইটানিকের মতো। " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "D0UdYPgvr2RO", + "colab_type": "code", + "colab": {}, + "outputId": "f2f921a2-ff5d-4a80-8c90-d852b4221966" + }, + "source": [ + "print(iris.DESCR)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Iris Plants Database\n", + "====================\n", + "\n", + "Notes\n", + "-----\n", + "Data Set Characteristics:\n", + " :Number of Instances: 150 (50 in each of three classes)\n", + " :Number of Attributes: 4 numeric, predictive attributes and the class\n", + " :Attribute Information:\n", + " - sepal length in cm\n", + " - sepal width in cm\n", + " - petal length in cm\n", + " - petal width in cm\n", + " - class:\n", + " - Iris-Setosa\n", + " - Iris-Versicolour\n", + " - Iris-Virginica\n", + " :Summary Statistics:\n", + "\n", + " ============== ==== ==== ======= ===== ====================\n", + " Min Max Mean SD Class Correlation\n", + " ============== ==== ==== ======= ===== ====================\n", + " sepal length: 4.3 7.9 5.84 0.83 0.7826\n", + " sepal width: 2.0 4.4 3.05 0.43 -0.4194\n", + " petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\n", + " petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\n", + " ============== ==== ==== ======= ===== ====================\n", + "\n", + " :Missing Attribute Values: None\n", + " :Class Distribution: 33.3% for each of 3 classes.\n", + " :Creator: R.A. Fisher\n", + " :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n", + " :Date: July, 1988\n", + "\n", + "This is a copy of UCI ML iris datasets.\n", + "http://archive.ics.uci.edu/ml/datasets/Iris\n", + "\n", + "The famous Iris database, first used by Sir R.A Fisher\n", + "\n", + "This is perhaps the best known database to be found in the\n", + "pattern recognition literature. Fisher's paper is a classic in the field and\n", + "is referenced frequently to this day. (See Duda & Hart, for example.) The\n", + "data set contains 3 classes of 50 instances each, where each class refers to a\n", + "type of iris plant. One class is linearly separable from the other 2; the\n", + "latter are NOT linearly separable from each other.\n", + "\n", + "References\n", + "----------\n", + " - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n", + " Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n", + " Mathematical Statistics\" (John Wiley, NY, 1950).\n", + " - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n", + " (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.\n", + " - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n", + " Structure and Classification Rule for Recognition in Partially Exposed\n", + " Environments\". IEEE Transactions on Pattern Analysis and Machine\n", + " Intelligence, Vol. PAMI-2, No. 1, 67-71.\n", + " - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\". IEEE Transactions\n", + " on Information Theory, May 1972, 431-433.\n", + " - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al\"s AUTOCLASS II\n", + " conceptual clustering system finds 3 classes in the data.\n", + " - Many, many more ...\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2Lb4l1cnr2RS", + "colab_type": "text" + }, + "source": [ + "### মেশিন লার্নিং মডেলের জন্য কি দরকার?\n", + "\n", + "দুটো জিনিস। তার আগে একটা ছবি দেখুন। এটা হচ্ছে সাইকিট-লার্ন এর ডাটা লেআউট। ধন্যবাদ, জেক ভ্যান্ডার প্লাসকে। মেশিন লার্নিং এর ভাষায় আমাদের দরকার ফিচার ম্যাট্রিক্স, আর টার্গেট ভেক্টর। সাইকিট-লার্ন আগে থেকে সেগুলোকে দুটো অ্যারে হিসেবে বানিয়ে রেখেছে। এখানে সেগুলোকে বলছি ডাটা অ্যারে আর টার্গেট অ্যারে। \n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NGVXSXMVr2RT", + "colab_type": "text" + }, + "source": [ + "### ফিচারগুলোর ম্যাট্রিক্স (X)\n", + "\n", + "এবার নিচের ছবিটা দেখুন। দুই ডাইমেনশনাল আইরিস ফুলের মাপ্গুলো হচ্ছে ফিচার ম্যাট্রিক্স। সেটাকে মেলান ওপরের ছবিটার বামের টেবিলের সাথে। এই দুই ডাইমেনশনাল অ্যারেটার shape হচ্ছে [৯, ৫], যেটা এখানে [n_samples, n_features]। এখানে সারিগুলো হচ্ছে একেকটা স্যাম্পল অবজেক্ট ওই ডাটাসেটে। এখানে আইরিস ডাটাসেটের ৫০টা ফুলের ডাটা আছে এই ফিচার অ্যারেতে। চারটা ফিচার মানে চারটা মাপ আমাদের ফুলের। সেগুলো আছে কলাম ধরে। সাইকিট-লার্ন কনভেনশন অনুযায়ী এই অ্যারেকে স্টোর করে ভ্যারিয়েবল বড় 'X' এ। কেন? বলছি সামনে। " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G64aW29xr2RU", + "colab_type": "text" + }, + "source": [ + "### টার্গেট অ্যারে (y)\n", + "\n", + "মডেলে ফিচারগুলোর ম্যাট্রিক্স (X) এর সাথে দরকার আমাদের টার্গেট অ্যারে, মানে আউটকাম ভ্যারিয়েবল। এটা সাধারণত: এক ডাইমেনশনাল হয়, লম্বা হয় ফিচারগুলোর ম্যাট্রিক্স (X) এর যতগুলো সারি থাকে। ওপরের ছবি অনুযায়ী অ্যারেটার shape হচ্ছে [৯, ১], যেটা এখানে [n_samples]। পরিসংখ্যানের ভাষায় এটা ডিপেন্ডেন্ট ভ্যারিয়েবল। কনভেনশন অনুযায়ী টার্গেট অ্যারেকে স্টোর করি টার্গেট অ্যারে (y)তে। লোয়ারকেস (y) হচ্ছে ডিপেনডেন্ট ভ্যারিয়েবল। অ্যারে (y) তার যেকোন পরিবর্তনের জন্য ফিচারগুলোর ম্যাট্রিক্স (X) এর ওপর নির্ভরশীল। এর মানে দাঁড়ালো ওই ফর্মুলার কথা। আমাদের অংকের ফাংশন অফ x, f(x)=y মানে ইনপুট x পাল্টালে আউটপুট y পাল্টাবে। বড় (X) ব্যবহার করার মানে হচ্ছে এটা হ্যান্ডেল করছে দুই ডাইমেনশনাল অ্যারে, আমরা যাকে বলছি ম্যাট্রিক্স। লোয়ারকেস y কারণ, আমাদের টার্গেট এক ডাইমেনশনাল অ্যারে, আমরা যাকে বলি ভেক্টর। " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WX2udP7ar2RW", + "colab_type": "text" + }, + "source": [ + "### ডাটার শেপ, মানে কতোটা ইনস্ট্যান্স?" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": true, + "id": "9YRsBd1yr2RX", + "colab_type": "code", + "colab": {} + }, + "source": [ + "n_samples, n_features = iris.data.shape" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "zv5fTj-Yr2Rf", + "colab_type": "code", + "colab": {}, + "outputId": "799a4dfe-72a2-4b94-a994-d2db59047e21" + }, + "source": [ + "n_samples" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "150" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 34 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "KLGy-714r2Rn", + "colab_type": "code", + "colab": {}, + "outputId": "e1072d30-5f35-4059-b027-1c3702c97ea7" + }, + "source": [ + "n_features" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "4" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 35 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "AOJaX2a-r2Rz", + "colab_type": "code", + "colab": {}, + "outputId": "861efd9e-caea-4724-a7b3-789fdf610d23" + }, + "source": [ + "print(\"Shape of data:\", iris['data'].shape)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Shape of data: (150, 4)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eeKGsRHur2R8", + "colab_type": "text" + }, + "source": [ + "কোন ডাটা মিসিং নেই " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "HKhi483_r2SA", + "colab_type": "code", + "colab": {}, + "outputId": "ddf9f5a4-232c-465c-8aa4-6e636f701c7b" + }, + "source": [ + "len(iris.target) == n_samples" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "True" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 37 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "id": "HdgkFYGVr2SD", + "colab_type": "text" + }, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eInUiuGnr2SF", + "colab_type": "text" + }, + "source": [ + "### ফিচারগুলোর নাম \n", + "\n", + "ওপরের ছবিতে চারটা ফিচারের নাম দেখেছি। চলুন দেখি সেগুলো আমাদের ডাটাসেট অবজেক্টে। iris এর পর ডট নোটেশন ব্যবহার করে ডাকি একটা \"কী\" ভ্যালুকে। feature_names হচ্ছে আমাদের iris.keys() থেকে পাওয়া একটা অ্যাট্রিবিউট।" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "W-mhiD8or2SG", + "colab_type": "code", + "colab": {}, + "outputId": "09d5038d-40ca-4fb5-ba7e-590c9ea270c3" + }, + "source": [ + "iris.feature_names" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['sepal length (cm)',\n", + " 'sepal width (cm)',\n", + " 'petal length (cm)',\n", + " 'petal width (cm)']" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 38 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9gR9AQ_0r2SJ", + "colab_type": "code", + "colab": {}, + "outputId": "db6d38eb-c3a7-417f-9812-bf7034857521" + }, + "source": [ + "print(iris['feature_names'])" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AdE6gUNEr2SL", + "colab_type": "text" + }, + "source": [ + "### টার্গেট অর্থাৎ কী প্রেডিক্ট করতে চাই আমরা?\n", + "\n", + "অনেকভাবেই করা সম্ভব। তবে print ফরম্যাটিং এ ভালো কাজ করে। " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zxi22TETr2SP", + "colab_type": "code", + "colab": {}, + "outputId": "5ba25603-ac13-4541-d01d-4d8b4986c46c" + }, + "source": [ + "iris.target_names" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array(['setosa', 'versicolor', 'virginica'],\n", + " dtype='\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vO1Dd8VJr2TD", + "colab_type": "text" + }, + "source": [ + "ফিচারের ম্যাট্রিক্স কি? (১ম ডাইমেনশন = অবজার্ভেশনের সংখ্যা, ২য় = ফিচারের সংখ্যা)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "czg5QrVRr2TE", + "colab_type": "code", + "colab": {}, + "outputId": "ee468fcf-bf2b-45cf-b155-743da46c4c2f" + }, + "source": [ + "print(iris.data.shape)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(150, 4)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RiD9Vwbqr2TK", + "colab_type": "text" + }, + "source": [ + "টার্গেট ম্যাট্রিক্স কি? (১ম ডাইমেনশন = লেবেল, টার্গেট, রেসপন্স)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "kgoRpjher2TL", + "colab_type": "code", + "colab": {}, + "outputId": "e2d54ab2-a523-4add-bda8-e9406505bb60" + }, + "source": [ + "print(iris.target.shape)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(150,)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ePg1gUgZr2TX", + "colab_type": "code", + "colab": {}, + "outputId": "d8df454c-fb81-4ddb-d861-27ad97e28cf5" + }, + "source": [ + "print(\"Shape of target:\", iris['target'].shape)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Shape of target: (150,)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "75lX7qNMr2Tb", + "colab_type": "text" + }, + "source": [ + "### সাইকিট-লার্ন এ ডাটা হ্যান্ডলিং এর নিয়ম \n", + "\n", + "১. এখানে \"ফিচার\" এবং \"রেসপন্স\" দুটো আলাদা অবজেক্ট \n", + "(আমাদের এখানে দেখুন, \"ফিচার\" এবং \"রেসপন্স\" মানে \"টার্গেট\" আলাদা অবজেক্ট)\n", + "\n", + "২. \"ফিচার\" এবং \"রেসপন্স\" দুটোকেই সংখ্যা হতে হবে \n", + "(আমাদের এখানে দুটোই সংখ্যার, দুটোর ম্যাট্রিক্স ডাইমেনশন হচ্ছে (১৫০ x ৪) এবং (১৫০ x ১)\n", + "\n", + "৩. \"ফিচার\" এবং \"রেসপন্স\" দুটোকেই \"নামপাই অ্যারে\" হতে হবে। \n", + "(আমাদের দুটো ফিচারই আছে \"নামপাই অ্যারে\"তে, বাকি ডাটা ডাটাসেট দরকার হলে সেটাকেও লোড করে নিতে হবে \"নামপাই অ্যারে\"তে)\n", + "\n", + "৪. \"ফিচার\" এবং \"রেসপন্স\" দুটোকেই স্পেসিফিক shape হতে হবে \n", + "\n", + "* ১৫০ x ৪ -> পুরো ডাটাসেট \n", + "* ১৫০ x ১ টার্গেটের জন্য \n", + "* ৪ x ১ ফিচারের জন্য \n", + "* আমরা ইচ্ছা করলে যেকোন ম্যাট্রিক্স পাল্টে নিতে পারি আমাদের দরকার মতো। যেমন np.tile(a, [4, 1]), মানে a হচ্ছে ম্যাট্রিক্স আর [4, 1] হচ্ছে ইনডেন্ট ম্যাট্রিক্স আরেক ডাইমেনশনে। " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "l0rk0Schr2Tc", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# ফিচার ম্যাট্রিক্স স্টোর করছি বড় \"X\"এ, মনে আছে f(x)=y কথা? x ইনপুট হলে y আউটপুট \n", + "X = iris.data\n", + "\n", + "# রেসপন্স ভেক্টর রাখছি \"y\" তে \n", + "y = iris.target" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "rJt5eHzsr2Ti", + "colab_type": "code", + "colab": {}, + "outputId": "e887d06d-a51b-495c-fa05-40872d17c549" + }, + "source": [ + "X" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[ 5.1, 3.5, 1.4, 0.2],\n", + " [ 4.9, 3. , 1.4, 0.2],\n", + " [ 4.7, 3.2, 1.3, 0.2],\n", + " [ 4.6, 3.1, 1.5, 0.2],\n", + " [ 5. , 3.6, 1.4, 0.2],\n", + " [ 5.4, 3.9, 1.7, 0.4],\n", + " [ 4.6, 3.4, 1.4, 0.3],\n", + " [ 5. , 3.4, 1.5, 0.2],\n", + " [ 4.4, 2.9, 1.4, 0.2],\n", + " [ 4.9, 3.1, 1.5, 0.1],\n", + " [ 5.4, 3.7, 1.5, 0.2],\n", + " [ 4.8, 3.4, 1.6, 0.2],\n", + " [ 4.8, 3. , 1.4, 0.1],\n", + " [ 4.3, 3. , 1.1, 0.1],\n", + " [ 5.8, 4. , 1.2, 0.2],\n", + " [ 5.7, 4.4, 1.5, 0.4],\n", + " [ 5.4, 3.9, 1.3, 0.4],\n", + " [ 5.1, 3.5, 1.4, 0.3],\n", + " [ 5.7, 3.8, 1.7, 0.3],\n", + " [ 5.1, 3.8, 1.5, 0.3],\n", + " [ 5.4, 3.4, 1.7, 0.2],\n", + " [ 5.1, 3.7, 1.5, 0.4],\n", + " [ 4.6, 3.6, 1. , 0.2],\n", + " [ 5.1, 3.3, 1.7, 0.5],\n", + " [ 4.8, 3.4, 1.9, 0.2],\n", + " [ 5. , 3. , 1.6, 0.2],\n", + " [ 5. , 3.4, 1.6, 0.4],\n", + " [ 5.2, 3.5, 1.5, 0.2],\n", + " [ 5.2, 3.4, 1.4, 0.2],\n", + " [ 4.7, 3.2, 1.6, 0.2],\n", + " [ 4.8, 3.1, 1.6, 0.2],\n", + " [ 5.4, 3.4, 1.5, 0.4],\n", + " [ 5.2, 4.1, 1.5, 0.1],\n", + " [ 5.5, 4.2, 1.4, 0.2],\n", + " [ 4.9, 3.1, 1.5, 0.1],\n", + " [ 5. , 3.2, 1.2, 0.2],\n", + " [ 5.5, 3.5, 1.3, 0.2],\n", + " [ 4.9, 3.1, 1.5, 0.1],\n", + " [ 4.4, 3. , 1.3, 0.2],\n", + " [ 5.1, 3.4, 1.5, 0.2],\n", + " [ 5. , 3.5, 1.3, 0.3],\n", + " [ 4.5, 2.3, 1.3, 0.3],\n", + " [ 4.4, 3.2, 1.3, 0.2],\n", + " [ 5. , 3.5, 1.6, 0.6],\n", + " [ 5.1, 3.8, 1.9, 0.4],\n", + " [ 4.8, 3. , 1.4, 0.3],\n", + " [ 5.1, 3.8, 1.6, 0.2],\n", + " [ 4.6, 3.2, 1.4, 0.2],\n", + " [ 5.3, 3.7, 1.5, 0.2],\n", + " [ 5. , 3.3, 1.4, 0.2],\n", + " [ 7. , 3.2, 4.7, 1.4],\n", + " [ 6.4, 3.2, 4.5, 1.5],\n", + " [ 6.9, 3.1, 4.9, 1.5],\n", + " [ 5.5, 2.3, 4. , 1.3],\n", + " [ 6.5, 2.8, 4.6, 1.5],\n", + " [ 5.7, 2.8, 4.5, 1.3],\n", + " [ 6.3, 3.3, 4.7, 1.6],\n", + " [ 4.9, 2.4, 3.3, 1. ],\n", + " [ 6.6, 2.9, 4.6, 1.3],\n", + " [ 5.2, 2.7, 3.9, 1.4],\n", + " [ 5. , 2. , 3.5, 1. ],\n", + " [ 5.9, 3. , 4.2, 1.5],\n", + " [ 6. , 2.2, 4. , 1. ],\n", + " [ 6.1, 2.9, 4.7, 1.4],\n", + " [ 5.6, 2.9, 3.6, 1.3],\n", + " [ 6.7, 3.1, 4.4, 1.4],\n", + " [ 5.6, 3. , 4.5, 1.5],\n", + " [ 5.8, 2.7, 4.1, 1. ],\n", + " [ 6.2, 2.2, 4.5, 1.5],\n", + " [ 5.6, 2.5, 3.9, 1.1],\n", + " [ 5.9, 3.2, 4.8, 1.8],\n", + " [ 6.1, 2.8, 4. , 1.3],\n", + " [ 6.3, 2.5, 4.9, 1.5],\n", + " [ 6.1, 2.8, 4.7, 1.2],\n", + " [ 6.4, 2.9, 4.3, 1.3],\n", + " [ 6.6, 3. , 4.4, 1.4],\n", + " [ 6.8, 2.8, 4.8, 1.4],\n", + " [ 6.7, 3. , 5. , 1.7],\n", + " [ 6. , 2.9, 4.5, 1.5],\n", + " [ 5.7, 2.6, 3.5, 1. ],\n", + " [ 5.5, 2.4, 3.8, 1.1],\n", + " [ 5.5, 2.4, 3.7, 1. ],\n", + " [ 5.8, 2.7, 3.9, 1.2],\n", + " [ 6. , 2.7, 5.1, 1.6],\n", + " [ 5.4, 3. , 4.5, 1.5],\n", + " [ 6. , 3.4, 4.5, 1.6],\n", + " [ 6.7, 3.1, 4.7, 1.5],\n", + " [ 6.3, 2.3, 4.4, 1.3],\n", + " [ 5.6, 3. , 4.1, 1.3],\n", + " [ 5.5, 2.5, 4. , 1.3],\n", + " [ 5.5, 2.6, 4.4, 1.2],\n", + " [ 6.1, 3. , 4.6, 1.4],\n", + " [ 5.8, 2.6, 4. , 1.2],\n", + " [ 5. , 2.3, 3.3, 1. ],\n", + " [ 5.6, 2.7, 4.2, 1.3],\n", + " [ 5.7, 3. , 4.2, 1.2],\n", + " [ 5.7, 2.9, 4.2, 1.3],\n", + " [ 6.2, 2.9, 4.3, 1.3],\n", + " [ 5.1, 2.5, 3. , 1.1],\n", + " [ 5.7, 2.8, 4.1, 1.3],\n", + " [ 6.3, 3.3, 6. , 2.5],\n", + " [ 5.8, 2.7, 5.1, 1.9],\n", + " [ 7.1, 3. , 5.9, 2.1],\n", + " [ 6.3, 2.9, 5.6, 1.8],\n", + " [ 6.5, 3. , 5.8, 2.2],\n", + " [ 7.6, 3. , 6.6, 2.1],\n", + " [ 4.9, 2.5, 4.5, 1.7],\n", + " [ 7.3, 2.9, 6.3, 1.8],\n", + " [ 6.7, 2.5, 5.8, 1.8],\n", + " [ 7.2, 3.6, 6.1, 2.5],\n", + " [ 6.5, 3.2, 5.1, 2. ],\n", + " [ 6.4, 2.7, 5.3, 1.9],\n", + " [ 6.8, 3. , 5.5, 2.1],\n", + " [ 5.7, 2.5, 5. , 2. ],\n", + " [ 5.8, 2.8, 5.1, 2.4],\n", + " [ 6.4, 3.2, 5.3, 2.3],\n", + " [ 6.5, 3. , 5.5, 1.8],\n", + " [ 7.7, 3.8, 6.7, 2.2],\n", + " [ 7.7, 2.6, 6.9, 2.3],\n", + " [ 6. , 2.2, 5. , 1.5],\n", + " [ 6.9, 3.2, 5.7, 2.3],\n", + " [ 5.6, 2.8, 4.9, 2. ],\n", + " [ 7.7, 2.8, 6.7, 2. ],\n", + " [ 6.3, 2.7, 4.9, 1.8],\n", + " [ 6.7, 3.3, 5.7, 2.1],\n", + " [ 7.2, 3.2, 6. , 1.8],\n", + " [ 6.2, 2.8, 4.8, 1.8],\n", + " [ 6.1, 3. , 4.9, 1.8],\n", + " [ 6.4, 2.8, 5.6, 2.1],\n", + " [ 7.2, 3. , 5.8, 1.6],\n", + " [ 7.4, 2.8, 6.1, 1.9],\n", + " [ 7.9, 3.8, 6.4, 2. ],\n", + " [ 6.4, 2.8, 5.6, 2.2],\n", + " [ 6.3, 2.8, 5.1, 1.5],\n", + " [ 6.1, 2.6, 5.6, 1.4],\n", + " [ 7.7, 3. , 6.1, 2.3],\n", + " [ 6.3, 3.4, 5.6, 2.4],\n", + " [ 6.4, 3.1, 5.5, 1.8],\n", + " [ 6. , 3. , 4.8, 1.8],\n", + " [ 6.9, 3.1, 5.4, 2.1],\n", + " [ 6.7, 3.1, 5.6, 2.4],\n", + " [ 6.9, 3.1, 5.1, 2.3],\n", + " [ 5.8, 2.7, 5.1, 1.9],\n", + " [ 6.8, 3.2, 5.9, 2.3],\n", + " [ 6.7, 3.3, 5.7, 2.5],\n", + " [ 6.7, 3. , 5.2, 2.3],\n", + " [ 6.3, 2.5, 5. , 1.9],\n", + " [ 6.5, 3. , 5.2, 2. ],\n", + " [ 6.2, 3.4, 5.4, 2.3],\n", + " [ 5.9, 3. , 5.1, 1.8]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 52 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "X93L-h3jr2Tp", + "colab_type": "code", + "colab": {}, + "outputId": "2f4adb7a-e069-49f7-a13f-afabb170d49c" + }, + "source": [ + "y" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 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