Update sklearn_transformers.py

my_custom_sklearn_transforms/isa

@@ -0,0 +1,63 @@
+from sklearn.base import BaseEstimator, TransformerMixin
+
+
+# All sklearn Transforms must have the `transform` and `fit` methods
+class DropColumns(BaseEstimator, TransformerMixin):
+    def __init__(self, columns):
+        self.columns = columns
+
+    def fit(self, X, y=None):
+        return self
+
+    def transform(self, X):
+        # Primeiro realizamos a cópia do dataframe 'X' de entrada
+        data = X.copy()
+        # Retornamos um novo dataframe sem as colunas indesejadas
+        return data.drop(labels=self.columns, axis='columns') def __init__(self, clusterer, classifier):
+        self.clusterer = clusterer
+        self.classifier = classifier
+def predict(self, X):
+        return self.classifier_.predict(X)
+def decision_function(self, X):
+        return self.classifier_.decision_function(X)
+def plot_scatter(X,  color, alpha=0.5):
+    return plt.scatter(X[:, 0],
+                       X[:, 1],
+                       c=color,
+                       alpha=alpha,
+                       edgecolor='k')
+X, y = make_blobs(n_samples=N_SAMPLES,
+                  cluster_std=[1.0, 1.0, 0.5],
+                  centers=[(-5, -5), (0, 0), (5, 5)],
+                  random_state=RANDOM_STATE)
+lusterer = AgglomerativeClustering(n_clusters=3)
+cluster_labels = clusterer.fit_predict(X)
+
+plt.figure(figsize=(12, 4))
+
+plt.subplot(131)
+plot_scatter(X, cluster_labels)
+plt.title("Ward Linkage")
+X_new, y_new = make_blobs(n_samples=10,
+                          centers=[(-7, -1), (-2, 4), (3, 6)],
+                          random_state=RANDOM_STATE)
+plot_scatter(X, cluster_labels)
+plot_scatter(X_new, 'black', 1)
+plt.title("Unknown instances")
+classifier = RandomForestClassifier(random_state=RANDOM_STATE)
+inductive_learner = InductiveClusterer(clusterer, classifier).fit(X)
+
+probable_clusters = inductive_learner.predict(X_new)
+plot_scatter(X, cluster_labels)
+plot_scatter(X_new, probable_clusters)
+x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
+y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
+xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),
+                     np.arange(y_min, y_max, 0.1))
+Z = inductive_learner.predict(np.c_[xx.ravel(), yy.ravel()])
+Z = Z.reshape(xx.shape)
+
+plt.contourf(xx, yy, Z, alpha=0.4)
+plt.title("Classify unknown instances")
+
+plt.show()