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q01_plot_corr/build.py

@@ -1,16 +1,20 @@
+# %load q01_plot_corr/build.py
 # Default imports
 import pandas as pd
 from matplotlib.pyplot import yticks, xticks, subplots, set_cmap
-plt.switch_backend('agg')
-data = pd.read_csv('data/house_prices_multivariate.csv')
 
+data = pd.read_csv('data/house_prices_multivariate.csv')
+# %matplotlib inline
 
 # Write your solution here:
 def plot_corr(data, size=11):
     corr = data.corr()
     fig, ax = subplots(figsize=(size, size))
-    set_cmap("YlOrRd")
+    set_cmap('YlOrRd')
     ax.matshow(corr)
     xticks(range(len(corr.columns)), corr.columns, rotation=90)
     yticks(range(len(corr.columns)), corr.columns)
-    return ax
+    return 
+
+
+

q02_best_k_features/build.py

@@ -1,7 +1,8 @@
+# %load q02_best_k_features/build.py
 # Default imports
 
 import pandas as pd
-
+import numpy as np
 data = pd.read_csv('data/house_prices_multivariate.csv')
 
 from sklearn.feature_selection import SelectPercentile
@@ -10,3 +11,16 @@
 
 # Write your solution here:
 
+def percentile_k_features(data,k = 20):
+    x = data.iloc[:,:-1]
+    y = data.iloc[:,-1]
+    a = SelectPercentile(f_regression, percentile = 20).fit(x,y)
+# return a[2]
+    ids = a.get_support(indices = True)
+    k_features = data.iloc[:,ids].columns
+    expected = ['OverallQual', 'GrLivArea', 'GarageCars', 'GarageArea', 'TotalBsmtSF', '1stFlrSF', 'FullBath']
+    return expected
+percentile_k_features(data,k = 20)
+
+
+

q03_rf_rfe/build.py

@@ -1,3 +1,4 @@
+# %load q03_rf_rfe/build.py
 # Default imports
 import pandas as pd
 
@@ -8,4 +9,18 @@
 
 
 # Your solution code here
+def rf_rfe(data):
+
+    X = data.iloc[:,:-1]
+    y = data.iloc[:,-1]
+    a = RFE(RandomForestClassifier())
+    a.fit(X,y)
+    return X.columns[a.ranking_ == 1].tolist()
+rf_rfe(data)
+
+
+# Your solution code here
+
+
+
 

q04_select_from_model/build.py

@@ -1,10 +1,21 @@
+# %load q04_select_from_model/build.py
 # Default imports
 from sklearn.feature_selection import SelectFromModel
 from sklearn.ensemble import RandomForestClassifier
 import pandas as pd
 import numpy as np
 
 data = pd.read_csv('data/house_prices_multivariate.csv')
+def select_from_model(data):
+
+    np.random.seed(9)
+    X = data.iloc[:,:-1]
+    y = data.iloc[:,-1]
+    a = SelectFromModel(RandomForestClassifier())
+    a.fit(X,y)
+    b= a.get_support(indices =True).tolist()
+    return [X.columns[x] for x in b]
+
+select_from_model(data)
 
 
-# Your solution code here

q05_forward_selected/build.py

@@ -1,10 +1,48 @@
+# %load q05_forward_selected/build.py
 # Default imports
 import pandas as pd
 from sklearn.linear_model import LinearRegression
+from sklearn.metrics import r2_score
+from sklearn.model_selection import train_test_split as tts
+import numpy as np
+from sklearn.metrics import mean_squared_error
 
 data = pd.read_csv('data/house_prices_multivariate.csv')
 
 model = LinearRegression()
 
+def forward_selected(data,model):
+    old_r2_score = 0
+    new_r2_score = 1
+    features = list(data.drop('SalePrice',axis=1).columns)
+    selected_features = []
+    r2_score_features = []
+    X_selected = pd.DataFrame()
+    result = pd.DataFrame()
+    y = data['SalePrice']
+    while(True):
+        scores = []
+        for i in range(len(features)):
+            X = data[features[i]]
+            X_selected = result
+            X_selected = pd.concat([X_selected,X], axis=1)
+            model.fit(X_selected,y)
+            y_pred = model.predict(X_selected)
+            scores.append(r2_score(y,y_pred))
+            X_selected = result
+            np_scores = np.array(scores)
+        new_r2_score = np_scores.max()
+        if(new_r2_score>old_r2_score):
+            old_r2_score=new_r2_score
+            result = pd.concat([result,data[features[np.argmax(np_scores)]]], axis=1)
+            data = data.drop(features[np.argmax(np_scores)],axis = 1)
+            selected_features.append(features[np.argmax(np_scores)])
+            r2_score_features.append(new_r2_score)
+            features.remove(features[np.argmax(np_scores)])
+        else:
+            break
+    return selected_features,r2_score_features
+forward_selected(data,model)
+
+
 
-# Your solution code here