df_new_3['X'].fillna(mode(df_new_3['X']).mode[0],inplace = True)
df_new_3=pd.read_csv('G:\\Datasets\\7z assignment\\Test\\machine3_answer.csv')
df_new_3=df_new_3.drop("Unnamed: 0",axis=1)
df_new_3.dtypes()
df_new_3['Y']= df_new_3['Y'].astype(float)
df_new_3['Y']=df_new_3['Y'].values
from sklearn import svm
svm_clf= svm.SVC(decision_function_shape='ovo')
svm_clf.fit(new_df_machine_1[new_predictor_machine1],new_df_machine_1['X'])
X = [[0], [1], [2], [3]]
Y= [0, 1, 2, 3]
clf = svm.SVC(decision_function_shape='ovo')
clf.fit(X, Y)
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovo', degree=3, gamma='auto', kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
dec = clf.decision_function([[1]])
dec.shape[1] # 4 classes: 4*3/2 = 6
6
clf.decision_function_shape = "ovr"
dec = clf.decision_function([[1]])
dec.shape[1] # 4 classes
4
class pca_doer:
def __init__(self):
self.X = np.loadtxt("../train/traindata.tsv", dtype=float)
self.y = np.loadtxt("../train/trainyvalues.txt", dtype=int)
self.testdata = np.loadtxt("../testa/testdata.tsv", dtype=float)
self.X_r = None
self.pca = PCA(n_components=2)
self.classifier=None
self.testyvals=None
self.test2dims=None
def do_pca(self):
self.X_r = self.pca.fit_transform(X)
self.test2dims = pca.transform(self.testdata)
print ("explained variance ration (first two components): %s" %str(pca.explained_variance_ratio_))
def make_figure_after_pca(self):
plt.figure()
for c, i, target_name in zip("rb", [0, 1], ["zeroes", "ones"]):
plt.scatter(self.X_r[self.y == i, 0], self.X_r[self.y == i, 1], c=c, label=target_name)
plt.show()
def train_adaboost(self):
d =DecisionTreeClassifier(class_weight="auto")
self.classifier = AdaBoostClassifier(d, n_estimators=250)
self.classifier.fit(self.X_r, self.y)
def train_extra_trees(self):
self.classifier = ExtraTreesClassifier(class_weight="auto", n_estimators=250)
self.classifier.fit(self.X_r, self.y)
def predict(self):
self.testyvals = self.classifier.predict(self.test2dims)
def decision_boundaries_2class_ada(self):
plot_colors = "br"
plot_step = 0.02
class_names = "01"
plt.figure(figsize=(10, 5))
plt.subplot(121)
x_min, x_max = self.X_r[:, 0].min() - 1, self.X_r[:, 0].max() + 1
y_min, y_max = self.X_r[:, 1].min() - 1, self.X_r[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step), np.arange(y_min, y_max, plot_step))
Z = self.classifier.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)
plt.axis("tight")
for i, n, c in zip(range(2), class_names, plot_colors):
idx = np.where(self.y == i)
plt.scatter(self.X_r[idx, 0], self.X_r[idx, 1], c=c, cmap=plt.cm.Paired, label="Class %s" % n)
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.legend(loc='upper right')
plt.xlabel('x')
plt.ylabel('y')
plt.title('Decision Boundary')
twoclass_output = self.classifier.decision_function(X_r)
plot_range = (twoclass_output.min(), twoclass_output.max())
plt.subplot(122)
for i, n, c in zip(range(2), class_names, plot_colors):
plt.hist(twoclass_output[self.y == i],bins=10,range=plot_range,facecolor=c,label='Class %s' % n, alpha=0.5)
x1, x2, y1, y2 = plt.axis()
plt.axis((x1, x2, y1, y2 * 1.2))
plt.legend(loc='upper right')
plt.ylabel('Samples')
plt.xlabel('Score')
plt.title('Decision Scores')
plt.tight_layout()
plt.subplots_adjust(wspace=0.35)
plt.show()
def draw_decision_boundary(self):
cg.plot_decision_boundary(self.classifier, self.X_r, self.y)
def draw_ROC(self):
cg.plot_ROC(self.classifier, self.X_r, self.y)
def print_scores(self):
cg.print_f1_precision_recall_score(self.classifier, self.X_r,self.y)
def plot_oob():
cg.plot_oob(self.classifier, self.X_r, self.y)
