Scikit-Learn

H
{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Scikit.ipynb","version":"0.3.2","provenance":[{"file_id":"1yPdtLIFlSPXAIyafhavMWLm2gz7W_SS_","timestamp":1568460540493}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"6g9dkBl3VYkN","colab_type":"code","colab":{}},"source":["import numpy as np"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"lhquzyjEViQI","colab_type":"code","colab":{}},"source":["from sklearn.preprocessing import MinMaxScaler"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"JG6KVbC3Vswg","colab_type":"code","colab":{}},"source":["data = np.random.randint(0,100,(10,2))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"YfErPqPaV8_R","colab_type":"code","outputId":"99b33ab0-8143-4cf9-dcc9-2612c745ee6a","executionInfo":{"status":"ok","timestamp":1568460465775,"user_tz":-330,"elapsed":906,"user":{"displayName":"HRITIK JAISWAL","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAIoT5asTvy-RaZPvDKRvz2bMxFBhU-1QvQZ2E4=s64","userId":"10596177819840519504"}},"colab":{"base_uri":"https://localhost:8080/","height":185}},"source":["data"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[90, 77],\n","       [82, 35],\n","       [ 9, 71],\n","       [20, 64],\n","       [39, 42],\n","       [74, 45],\n","       [35, 37],\n","       [92, 64],\n","       [49,  0],\n","       [11, 63]])"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"ac1laNdHWAGJ","colab_type":"code","outputId":"331b639b-53ab-4efe-b1c0-7ed7815fb13b","executionInfo":{"status":"ok","timestamp":1568460469139,"user_tz":-330,"elapsed":1198,"user":{"displayName":"HRITIK JAISWAL","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAIoT5asTvy-RaZPvDKRvz2bMxFBhU-1QvQZ2E4=s64","userId":"10596177819840519504"}},"colab":{"base_uri":"https://localhost:8080/","height":404}},"source":["import matplotlib\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from mpl_toolkits.mplot3d import Axes3D\n","x=data[:,0]\n","y=data[:,1]\n","\n","plt.figure(figsize=(8,6))\n","plt.plot(x,y,'r')\n","\n","plt.xlabel('x')\n","plt.ylabel('y')\n","\n","plt.title(r\"Plot of y\")\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAfEAAAGDCAYAAAA72Cm3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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yZJ2Y8oG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kLpWxZnsbmCzsFunThBemOHbYjscp6Er8kmNsBPJlc2PYNgO/SFLZtEpGPr/b1msR9yEsv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\nklRShrgkSSX1P3o5k75gLxyEAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 576x432 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"hJWyTD4zYLLT","colab_type":"code","colab":{}},"source":["scaler_model=MinMaxScaler()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"D0hNIBM7YTKx","colab_type":"code","outputId":"bb52adc2-ac40-4f93-bd38-630448f80d63","executionInfo":{"status":"ok","timestamp":1568460473730,"user_tz":-330,"elapsed":892,"user":{"displayName":"HRITIK JAISWAL","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAIoT5asTvy-RaZPvDKRvz2bMxFBhU-1QvQZ2E4=s64","userId":"10596177819840519504"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["scaler_model.fit(data)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["MinMaxScaler(copy=True, feature_range=(0, 1))"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"id":"Qty9LRQvYc0R","colab_type":"code","colab":{}},"source":["result=scaler_model.transform(data)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"wuNd4Sc8abfl","colab_type":"code","colab":{}},"source":["#scaler_model_fit_transform(data) Alternative to the above 2 steps"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"8grzmSwTYlf5","colab_type":"code","outputId":"d13268b8-610d-4a32-aed7-ab6e56d21460","executionInfo":{"status":"ok","timestamp":1568460478853,"user_tz":-330,"elapsed":577,"user":{"displayName":"HRITIK JAISWAL","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAIoT5asTvy-RaZPvDKRvz2bMxFBhU-1QvQZ2E4=s64","userId":"10596177819840519504"}},"colab":{"base_uri":"https://localhost:8080/","height":185}},"source":["result"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.97590361, 1.        ],\n","       [0.87951807, 0.45454545],\n","       [0.        , 0.92207792],\n","       [0.13253012, 0.83116883],\n","       [0.36144578, 0.54545455],\n","       [0.78313253, 0.58441558],\n","       [0.31325301, 0.48051948],\n","       [1.        , 0.83116883],\n","       [0.48192771, 0.        ],\n","       [0.02409639, 0.81818182]])"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"id":"aTZm_6DnYrxJ","colab_type":"code","outputId":"49ff7ed6-342f-4c8a-f4d8-4756353fc1af","executionInfo":{"status":"ok","timestamp":1568460481825,"user_tz":-330,"elapsed":1461,"user":{"displayName":"HRITIK JAISWAL","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAIoT5asTvy-RaZPvDKRvz2bMxFBhU-1QvQZ2E4=s64","userId":"10596177819840519504"}},"colab":{"base_uri":"https://localhost:8080/","height":404}},"source":["x=result[:,0]\n","y=result[:,1]\n","\n","plt.figure(figsize=(8,6))\n","plt.plot(x,y,'r')\n","\n","plt.xlabel('x')\n","plt.ylabel('y')\n","\n","plt.title(r\"Plot of y\")\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAfUAAAGDCAYAAAAyM4nNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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jx/PhA5w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size 576x432 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"2G26J6wKZLqR","colab_type":"code","outputId":"2df33442-698a-4808-ba01-042777f257db","executionInfo":{"status":"ok","timestamp":1568460485085,"user_tz":-330,"elapsed":887,"user":{"displayName":"HRITIK JAISWAL","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAIoT5asTvy-RaZPvDKRvz2bMxFBhU-1QvQZ2E4=s64","userId":"10596177819840519504"}},"colab":{"base_uri":"https://localhost:8080/","height":185}},"source":["data"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[90, 77],\n","       [82, 35],\n","       [ 9, 71],\n","       [20, 64],\n","       [39, 42],\n","       [74, 45],\n","       [35, 37],\n","       [92, 64],\n","       [49,  0],\n","       [11, 63]])"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"id":"R2hhD0SSaF4B","colab_type":"code","colab":{}},"source":["import pandas as pd"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"JDEZ4kXxbCqL","colab_type":"code","colab":{}},"source":["data= pd.DataFrame(data=np.random.randint(0,101,(50,4)),columns=['f1','f2','f3','label'])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"QY4DseBWbncC","colab_type":"code","outputId":"9ef17ea2-45d9-4d07-b4f8-ac19738292ad","executionInfo":{"status":"ok","timestamp":1568460503548,"user_tz":-330,"elapsed":958,"user":{"displayName":"HRITIK JAISWAL","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAIoT5asTvy-RaZPvDKRvz2bMxFBhU-1QvQZ2E4=s64","userId":"10596177819840519504"}},"colab":{"base_uri":"https://localhost:8080/","height":195}},"source":["data.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>f1</th>\n","      <th>f2</th>\n","      <th>f3</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>59</td>\n","      <td>38</td>\n","      <td>9</td>\n","      <td>67</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>33</td>\n","      <td>33</td>\n","      <td>57</td>\n","      <td>42</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>73</td>\n","      <td>68</td>\n","      <td>56</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>75</td>\n","      <td>27</td>\n","      <td>41</td>\n","      <td>86</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>33</td>\n","      <td>20</td>\n","      <td>0</td>\n","      <td>100</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   f1  f2  f3  label\n","0  59  38   9     67\n","1  33  33  57     42\n","2   0  73  68     56\n","3  75  27  41     86\n","4  33  20   0    100"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"code","metadata":{"id":"YfMIooQpbtoa","colab_type":"code","colab":{}},"source":["x=data[['f1','f2','f3']]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"btch2FNmdu5-","colab_type":"code","colab":{}},"source":["y=data['label']"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"-wRYDQ3zc2xa","colab_type":"code","colab":{}},"source":["from sklearn.model_selection import train_test_split"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"S-i3LitTdDGi","colab_type":"code","colab":{}},"source":["X_train,X_test,Y_train,Y_test=train_test_split(x,y,test_size=0.3,random_state=101)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"dWrrXmWed3YS","colab_type":"code","outputId":"d36a1062-db1a-427c-f8ea-fcea680e90ae","executionInfo":{"status":"ok","timestamp":1568460511994,"user_tz":-330,"elapsed":567,"user":{"displayName":"HRITIK JAISWAL","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAIoT5asTvy-RaZPvDKRvz2bMxFBhU-1QvQZ2E4=s64","userId":"10596177819840519504"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["X_train.shape\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(35, 3)"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"code","metadata":{"id":"KL6Rq_wbeAtq","colab_type":"code","outputId":"37d82e9f-e19a-464d-a202-a66cbfe3dbde","executionInfo":{"status":"ok","timestamp":1568459592491,"user_tz":-330,"elapsed":912,"user":{"displayName":"PRATIK MERCHANT","photoUrl":"","userId":"11573801413053499781"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["X_test.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(15, 3)"]},"metadata":{"tags":[]},"execution_count":40}]},{"cell_type":"code","metadata":{"id":"GlMYWMQ_eFyL","colab_type":"code","outputId":"ebcbb6e5-1fb5-4f97-ba85-94d923b2758a","executionInfo":{"status":"ok","timestamp":1568460517795,"user_tz":-330,"elapsed":887,"user":{"displayName":"HRITIK JAISWAL","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAIoT5asTvy-RaZPvDKRvz2bMxFBhU-1QvQZ2E4=s64","userId":"10596177819840519504"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["Y_train.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(35,)"]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"code","metadata":{"id":"nMRHCzPveIFl","colab_type":"code","outputId":"75053829-50ce-4e82-8dd6-84ae9d6d8c27","executionInfo":{"status":"ok","timestamp":1568460519257,"user_tz":-330,"elapsed":703,"user":{"displayName":"HRITIK JAISWAL","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAIoT5asTvy-RaZPvDKRvz2bMxFBhU-1QvQZ2E4=s64","userId":"10596177819840519504"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["Y_test.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(15,)"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"code","metadata":{"id":"ETyQVOxxeouC","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}
이 알고리즘에 대해
import numpy as np
from sklearn.preprocessing import MinMaxScaler
data = np.random.randint(0,100,(10,2))
data
array([[90, 77],
       [82, 35],
       [ 9, 71],
       [20, 64],
       [39, 42],
       [74, 45],
       [35, 37],
       [92, 64],
       [49,  0],
       [11, 63]])
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
x=data[:,0]
y=data[:,1]

plt.figure(figsize=(8,6))
plt.plot(x,y,'r')

plt.xlabel('x')
plt.ylabel('y')

plt.title(r"Plot of y")
plt.show()
scaler_model=MinMaxScaler()
scaler_model.fit(data)
MinMaxScaler(copy=True, feature_range=(0, 1))
result=scaler_model.transform(data)
#scaler_model_fit_transform(data) Alternative to the above 2 steps
result
array([[0.97590361, 1.        ],
       [0.87951807, 0.45454545],
       [0.        , 0.92207792],
       [0.13253012, 0.83116883],
       [0.36144578, 0.54545455],
       [0.78313253, 0.58441558],
       [0.31325301, 0.48051948],
       [1.        , 0.83116883],
       [0.48192771, 0.        ],
       [0.02409639, 0.81818182]])
x=result[:,0]
y=result[:,1]

plt.figure(figsize=(8,6))
plt.plot(x,y,'r')

plt.xlabel('x')
plt.ylabel('y')

plt.title(r"Plot of y")
plt.show()
data
array([[90, 77],
       [82, 35],
       [ 9, 71],
       [20, 64],
       [39, 42],
       [74, 45],
       [35, 37],
       [92, 64],
       [49,  0],
       [11, 63]])
import pandas as pd
data= pd.DataFrame(data=np.random.randint(0,101,(50,4)),columns=['f1','f2','f3','label'])
data.head()
f1 f2 f3 label
0 59 38 9 67
1 33 33 57 42
2 0 73 68 56
3 75 27 41 86
4 33 20 0 100
x=data[['f1','f2','f3']]
y=data['label']
from sklearn.model_selection import train_test_split
X_train,X_test,Y_train,Y_test=train_test_split(x,y,test_size=0.3,random_state=101)
X_train.shape
(35, 3)
X_test.shape
(15, 3)
Y_train.shape
(35,)
Y_test.shape
(15,)