The Algorithms logo
The Algorithms
关于捐赠

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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\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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\nbupmZmYV4aZuZmZWEW7qZmZmFfE/2S4BVzeOgWgAAAAASUVORK5CYII=\n","text/plain":["<Figure 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":[]}]}
关于这个算法
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,)