Issue#90

Code-Sleep-Python/Classification/code.py

@@ -1,88 +1,76 @@
+import random
+
+import matplotlib.pyplot as plt
+import numpy as np
 import pandas as pd
-data = pd.read_csv('https://s3.amazonaws.com/demo-datasets/wine.csv')
+import sklearn.decomposition
+from matplotlib.colors import ListedColormap
+#  More accuracy
+from sklearn.neighbors import KNeighborsClassifier
 
 
+def accuracy(predictions, outcomes):
+    # Enter your code here!
+    occur = 0
+    v = np.array(predictions) == np.array(outcomes)
+    occur = np.sum(v)
+    return occur
 
-df2 = data.drop('color', axis=1) # color is redundant
 
+data = pd.read_csv('https://s3.amazonaws.com/demo-datasets/wine.csv')
 
-import numpy as np
+df2 = data.drop('color', axis=1)  # color is redundant
 
+numeric_data = df2.values
 
+numeric_data = (numeric_data - np.mean(numeric_data)) / (np.std(numeric_data))
 
-numeric_data =  (numeric_data - np.mean(numeric_data))/(np.std(numeric_data))  
 
-import sklearn.decomposition
 pca = sklearn.decomposition.PCA(n_components=2)
 principal_components = pca.fit(numeric_data).transform(numeric_data)
 
-
-
-
-
-import matplotlib.pyplot as plt
-from matplotlib.colors import ListedColormap
-from matplotlib.backends.backend_pdf import PdfPages
 observation_colormap = ListedColormap(['red', 'blue'])
-x = principal_components[:,0]
-y = principal_components[:,1]
+x = principal_components[:, 0]
+y = principal_components[:, 1]
 
 plt.title("Principal Components of Wine")
-plt.scatter(x, y, alpha = 0.2,
-    c = data['high_quality'], cmap = observation_colormap, edgecolors = 'none')
-plt.xlim(-8, 8); plt.ylim(-8, 8)
-plt.xlabel("Principal Component 1"); plt.ylabel("Principal Component 2")
+plt.scatter(x, y, alpha=0.2,
+            c=data['high_quality'], cmap=observation_colormap,
+            edgecolors='none')
+plt.xlim(-8, 8)
+plt.ylim(-8, 8)
+plt.xlabel("Principal Component 1")
+plt.ylabel("Principal Component 2")
 plt.show()
 
+x = np.array([1, 2, 3])
+y = np.array([1, 2, 4])
 
+print(accuracy(x, y))
 
+print(accuracy([], data["high_quality"]))
 
-
-def accuracy(predictions, outcomes):
-    # Enter your code here!
-    occur =0
-    for i in range(len(predictions)):
-        if(predictions[i] == outcomes[i]):
-            occur += 1
-    return occur
-
-
-x = np.array([1,2,3])
-y = np.array([1,2,4])
-
-print (accuracy(x,y))
-
-
-print(accuracy(0,data["high_quality"]))
-
-
-##############################   More accuracy
-from sklearn.neighbors import KNeighborsClassifier
-knn = KNeighborsClassifier(n_neighbors = 5)
+knn = KNeighborsClassifier(n_neighbors=5)
 knn.fit(numeric_data, data['high_quality'])
 # Enter your code here!
 
 library_predictions = knn.predict(numeric_data)
 
-print(accuracy(library_predictions,data['high_quality']))
+print(accuracy(library_predictions, data['high_quality']))
 
-################
 n_rows = data.shape[0]
 
+print()
 random.seed(123)
 selection = random.sample(range(n_rows), 10)
 
-
 predictors = np.array(numeric_data)
 training_indices = [i for i in range(len(predictors)) if i not in selection]
 outcomes = np.array(data["high_quality"])
 
-my_predictions = [knn_predict(p, predictors[training_indices,:], outcomes, k=5) for p in predictors[selection]]
-percentage = accuracy(my_predictions,data.high_quality[selection])
+my_predictions = [
+    knn.predict(predictors[training_indices, :]) for p in
+    predictors[selection]]
+percentage = accuracy(my_predictions, outcomes)
 
 print(percentage)
-
-
-
-
-