Python 3.x 我无法使用命令conda install adspy在Anaconda中安装adspy_共享_实用程序
我想在jupyter笔记本中使用adspy_共享_实用程序包。我在windows 7上安装了Anaconda3。 我无法使用命令在Anaconda中安装adspy_共享_实用程序 康达安装adspy。Python 3.x 我无法使用命令conda install adspy在Anaconda中安装adspy_共享_实用程序,python-3.x,Python 3.x,我想在jupyter笔记本中使用adspy_共享_实用程序包。我在windows 7上安装了Anaconda3。 我无法使用命令在Anaconda中安装adspy_共享_实用程序 康达安装adspy。 我得到的错误是“以下软件包无法从当前频道获得。”@nehaadspy\u shared\u实用程序不是python库。它实际上是由Coursera上的应用机器学习课程的作者创建的自定义函数的集合。下面给出了adspy\u shared\u utilities.py源代码 复制下面的代码并将其另存为
我得到的错误是“以下软件包无法从当前频道获得。”@neha
adspy\u shared\u实用程序adspy\u shared\u utilities.pyadspy\u shared\u utilities.pyhttps://wmhbvoermnyeamhbufpsmv.coursera-apps.org/edit/adspy_shared_utilities.pyimport numpy
import pandas as pd
import seaborn as sn
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.colors import ListedColormap, BoundaryNorm
from sklearn import neighbors
import matplotlib.patches as mpatches
import graphviz
from sklearn.tree import export_graphviz
import matplotlib.patches as mpatches
def load_crime_dataset():
# Communities and Crime dataset for regression
# https://archive.ics.uci.edu/ml/datasets/Communities+and+Crime+Unnormalized
crime = pd.read_table('readonly/CommViolPredUnnormalizedData.txt', sep=',', na_values='?')
# remove features with poor coverage or lower relevance, and keep ViolentCrimesPerPop target column
columns_to_keep = [5, 6] + list(range(11,26)) + list(range(32, 103)) + [145]
crime = crime.ix[:,columns_to_keep].dropna()
X_crime = crime.ix[:,range(0,88)]
y_crime = crime['ViolentCrimesPerPop']
return (X_crime, y_crime)
def plot_decision_tree(clf, feature_names, class_names):
# This function requires the pydotplus module and assumes it's been installed.
# In some cases (typically under Windows) even after running conda install, there is a problem where the
# pydotplus module is not found when running from within the notebook environment. The following code
# may help to guarantee the module is installed in the current notebook environment directory.
#
# import sys; sys.executable
# !{sys.executable} -m pip install pydotplus
export_graphviz(clf, out_file="adspy_temp.dot", feature_names=feature_names, class_names=class_names, filled = True, impurity = False)
with open("adspy_temp.dot") as f:
dot_graph = f.read()
# Alternate method using pydotplus, if installed.
# graph = pydotplus.graphviz.graph_from_dot_data(dot_graph)
# return graph.create_png()
return graphviz.Source(dot_graph)
def plot_feature_importances(clf, feature_names):
c_features = len(feature_names)
plt.barh(range(c_features), clf.feature_importances_)
plt.xlabel("Feature importance")
plt.ylabel("Feature name")
plt.yticks(numpy.arange(c_features), feature_names)
def plot_labelled_scatter(X, y, class_labels):
num_labels = len(class_labels)
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
marker_array = ['o', '^', '*']
color_array = ['#FFFF00', '#00AAFF', '#000000', '#FF00AA']
cmap_bold = ListedColormap(color_array)
bnorm = BoundaryNorm(numpy.arange(0, num_labels + 1, 1), ncolors=num_labels)
plt.figure()
plt.scatter(X[:, 0], X[:, 1], s=65, c=y, cmap=cmap_bold, norm = bnorm, alpha = 0.40, edgecolor='black', lw = 1)
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
h = []
for c in range(0, num_labels):
h.append(mpatches.Patch(color=color_array[c], label=class_labels[c]))
plt.legend(handles=h)
plt.show()
def plot_class_regions_for_classifier_subplot(clf, X, y, X_test, y_test, title, subplot, target_names = None, plot_decision_regions = True):
numClasses = numpy.amax(y) + 1
color_list_light = ['#FFFFAA', '#EFEFEF', '#AAFFAA', '#AAAAFF']
color_list_bold = ['#EEEE00', '#000000', '#00CC00', '#0000CC']
cmap_light = ListedColormap(color_list_light[0:numClasses])
cmap_bold = ListedColormap(color_list_bold[0:numClasses])
h = 0.03
k = 0.5
x_plot_adjust = 0.1
y_plot_adjust = 0.1
plot_symbol_size = 50
x_min = X[:, 0].min()
x_max = X[:, 0].max()
y_min = X[:, 1].min()
y_max = X[:, 1].max()
x2, y2 = numpy.meshgrid(numpy.arange(x_min-k, x_max+k, h), numpy.arange(y_min-k, y_max+k, h))
P = clf.predict(numpy.c_[x2.ravel(), y2.ravel()])
P = P.reshape(x2.shape)
if plot_decision_regions:
subplot.contourf(x2, y2, P, cmap=cmap_light, alpha = 0.8)
subplot.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, s=plot_symbol_size, edgecolor = 'black')
subplot.set_xlim(x_min - x_plot_adjust, x_max + x_plot_adjust)
subplot.set_ylim(y_min - y_plot_adjust, y_max + y_plot_adjust)
if (X_test is not None):
subplot.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cmap_bold, s=plot_symbol_size, marker='^', edgecolor = 'black')
train_score = clf.score(X, y)
test_score = clf.score(X_test, y_test)
title = title + "\nTrain score = {:.2f}, Test score = {:.2f}".format(train_score, test_score)
subplot.set_title(title)
if (target_names is not None):
legend_handles = []
for i in range(0, len(target_names)):
patch = mpatches.Patch(color=color_list_bold[i], label=target_names[i])
legend_handles.append(patch)
subplot.legend(loc=0, handles=legend_handles)
def plot_class_regions_for_classifier(clf, X, y, X_test=None, y_test=None, title=None, target_names = None, plot_decision_regions = True):
numClasses = numpy.amax(y) + 1
color_list_light = ['#FFFFAA', '#EFEFEF', '#AAFFAA', '#AAAAFF']
color_list_bold = ['#EEEE00', '#000000', '#00CC00', '#0000CC']
cmap_light = ListedColormap(color_list_light[0:numClasses])
cmap_bold = ListedColormap(color_list_bold[0:numClasses])
h = 0.03
k = 0.5
x_plot_adjust = 0.1
y_plot_adjust = 0.1
plot_symbol_size = 50
x_min = X[:, 0].min()
x_max = X[:, 0].max()
y_min = X[:, 1].min()
y_max = X[:, 1].max()
x2, y2 = numpy.meshgrid(numpy.arange(x_min-k, x_max+k, h), numpy.arange(y_min-k, y_max+k, h))
P = clf.predict(numpy.c_[x2.ravel(), y2.ravel()])
P = P.reshape(x2.shape)
plt.figure()
if plot_decision_regions:
plt.contourf(x2, y2, P, cmap=cmap_light, alpha = 0.8)
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, s=plot_symbol_size, edgecolor = 'black')
plt.xlim(x_min - x_plot_adjust, x_max + x_plot_adjust)
plt.ylim(y_min - y_plot_adjust, y_max + y_plot_adjust)
if (X_test is not None):
plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cmap_bold, s=plot_symbol_size, marker='^', edgecolor = 'black')
train_score = clf.score(X, y)
test_score = clf.score(X_test, y_test)
title = title + "\nTrain score = {:.2f}, Test score = {:.2f}".format(train_score, test_score)
if (target_names is not None):
legend_handles = []
for i in range(0, len(target_names)):
patch = mpatches.Patch(color=color_list_bold[i], label=target_names[i])
legend_handles.append(patch)
plt.legend(loc=0, handles=legend_handles)
if (title is not None):
plt.title(title)
plt.show()
def plot_fruit_knn(X, y, n_neighbors, weights):
X_mat = X[['height', 'width']].as_matrix()
y_mat = y.as_matrix()
# Create color maps
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF','#AFAFAF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF','#AFAFAF'])
clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
clf.fit(X_mat, y_mat)
# Plot the decision boundary by assigning a color in the color map
# to each mesh point.
mesh_step_size = .01 # step size in the mesh
plot_symbol_size = 50
x_min, x_max = X_mat[:, 0].min() - 1, X_mat[:, 0].max() + 1
y_min, y_max = X_mat[:, 1].min() - 1, X_mat[:, 1].max() + 1
xx, yy = numpy.meshgrid(numpy.arange(x_min, x_max, mesh_step_size),
numpy.arange(y_min, y_max, mesh_step_size))
Z = clf.predict(numpy.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure()
plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
# Plot training points
plt.scatter(X_mat[:, 0], X_mat[:, 1], s=plot_symbol_size, c=y, cmap=cmap_bold, edgecolor = 'black')
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
patch0 = mpatches.Patch(color='#FF0000', label='apple')
patch1 = mpatches.Patch(color='#00FF00', label='mandarin')
patch2 = mpatches.Patch(color='#0000FF', label='orange')
patch3 = mpatches.Patch(color='#AFAFAF', label='lemon')
plt.legend(handles=[patch0, patch1, patch2, patch3])
plt.xlabel('height (cm)')
plt.ylabel('width (cm)')
plt.show()
def plot_two_class_knn(X, y, n_neighbors, weights, X_test, y_test):
X_mat = X
y_mat = y
# Create color maps
cmap_light = ListedColormap(['#FFFFAA', '#AAFFAA', '#AAAAFF','#EFEFEF'])
cmap_bold = ListedColormap(['#FFFF00', '#00FF00', '#0000FF','#000000'])
clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
clf.fit(X_mat, y_mat)
# Plot the decision boundary by assigning a color in the color map
# to each mesh point.
mesh_step_size = .01 # step size in the mesh
plot_symbol_size = 50
x_min, x_max = X_mat[:, 0].min() - 1, X_mat[:, 0].max() + 1
y_min, y_max = X_mat[:, 1].min() - 1, X_mat[:, 1].max() + 1
xx, yy = numpy.meshgrid(numpy.arange(x_min, x_max, mesh_step_size),
numpy.arange(y_min, y_max, mesh_step_size))
Z = clf.predict(numpy.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure()
plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
# Plot training points
plt.scatter(X_mat[:, 0], X_mat[:, 1], s=plot_symbol_size, c=y, cmap=cmap_bold, edgecolor = 'black')
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
title = "Neighbors = {}".format(n_neighbors)
if (X_test is not None):
train_score = clf.score(X_mat, y_mat)
test_score = clf.score(X_test, y_test)
title = title + "\nTrain score = {:.2f}, Test score = {:.2f}".format(train_score, test_score)
patch0 = mpatches.Patch(color='#FFFF00', label='class 0')
patch1 = mpatches.Patch(color='#000000', label='class 1')
plt.legend(handles=[patch0, patch1])
plt.xlabel('Feature 0')
plt.ylabel('Feature 1')
plt.title(title)
plt.show()
@neha
adspy\u共享\u实用程序adspy\u shared\u utilities.pyadspy\u shared\u utilities.pyhttps://wmhbvoermnyeamhbufpsmv.coursera-apps.org/edit/adspy_shared_utilities.pyimport numpy
import pandas as pd
import seaborn as sn
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.colors import ListedColormap, BoundaryNorm
from sklearn import neighbors
import matplotlib.patches as mpatches
import graphviz
from sklearn.tree import export_graphviz
import matplotlib.patches as mpatches
def load_crime_dataset():
# Communities and Crime dataset for regression
# https://archive.ics.uci.edu/ml/datasets/Communities+and+Crime+Unnormalized
crime = pd.read_table('readonly/CommViolPredUnnormalizedData.txt', sep=',', na_values='?')
# remove features with poor coverage or lower relevance, and keep ViolentCrimesPerPop target column
columns_to_keep = [5, 6] + list(range(11,26)) + list(range(32, 103)) + [145]
crime = crime.ix[:,columns_to_keep].dropna()
X_crime = crime.ix[:,range(0,88)]
y_crime = crime['ViolentCrimesPerPop']
return (X_crime, y_crime)
def plot_decision_tree(clf, feature_names, class_names):
# This function requires the pydotplus module and assumes it's been installed.
# In some cases (typically under Windows) even after running conda install, there is a problem where the
# pydotplus module is not found when running from within the notebook environment. The following code
# may help to guarantee the module is installed in the current notebook environment directory.
#
# import sys; sys.executable
# !{sys.executable} -m pip install pydotplus
export_graphviz(clf, out_file="adspy_temp.dot", feature_names=feature_names, class_names=class_names, filled = True, impurity = False)
with open("adspy_temp.dot") as f:
dot_graph = f.read()
# Alternate method using pydotplus, if installed.
# graph = pydotplus.graphviz.graph_from_dot_data(dot_graph)
# return graph.create_png()
return graphviz.Source(dot_graph)
def plot_feature_importances(clf, feature_names):
c_features = len(feature_names)
plt.barh(range(c_features), clf.feature_importances_)
plt.xlabel("Feature importance")
plt.ylabel("Feature name")
plt.yticks(numpy.arange(c_features), feature_names)
def plot_labelled_scatter(X, y, class_labels):
num_labels = len(class_labels)
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
marker_array = ['o', '^', '*']
color_array = ['#FFFF00', '#00AAFF', '#000000', '#FF00AA']
cmap_bold = ListedColormap(color_array)
bnorm = BoundaryNorm(numpy.arange(0, num_labels + 1, 1), ncolors=num_labels)
plt.figure()
plt.scatter(X[:, 0], X[:, 1], s=65, c=y, cmap=cmap_bold, norm = bnorm, alpha = 0.40, edgecolor='black', lw = 1)
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
h = []
for c in range(0, num_labels):
h.append(mpatches.Patch(color=color_array[c], label=class_labels[c]))
plt.legend(handles=h)
plt.show()
def plot_class_regions_for_classifier_subplot(clf, X, y, X_test, y_test, title, subplot, target_names = None, plot_decision_regions = True):
numClasses = numpy.amax(y) + 1
color_list_light = ['#FFFFAA', '#EFEFEF', '#AAFFAA', '#AAAAFF']
color_list_bold = ['#EEEE00', '#000000', '#00CC00', '#0000CC']
cmap_light = ListedColormap(color_list_light[0:numClasses])
cmap_bold = ListedColormap(color_list_bold[0:numClasses])
h = 0.03
k = 0.5
x_plot_adjust = 0.1
y_plot_adjust = 0.1
plot_symbol_size = 50
x_min = X[:, 0].min()
x_max = X[:, 0].max()
y_min = X[:, 1].min()
y_max = X[:, 1].max()
x2, y2 = numpy.meshgrid(numpy.arange(x_min-k, x_max+k, h), numpy.arange(y_min-k, y_max+k, h))
P = clf.predict(numpy.c_[x2.ravel(), y2.ravel()])
P = P.reshape(x2.shape)
if plot_decision_regions:
subplot.contourf(x2, y2, P, cmap=cmap_light, alpha = 0.8)
subplot.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, s=plot_symbol_size, edgecolor = 'black')
subplot.set_xlim(x_min - x_plot_adjust, x_max + x_plot_adjust)
subplot.set_ylim(y_min - y_plot_adjust, y_max + y_plot_adjust)
if (X_test is not None):
subplot.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cmap_bold, s=plot_symbol_size, marker='^', edgecolor = 'black')
train_score = clf.score(X, y)
test_score = clf.score(X_test, y_test)
title = title + "\nTrain score = {:.2f}, Test score = {:.2f}".format(train_score, test_score)
subplot.set_title(title)
if (target_names is not None):
legend_handles = []
for i in range(0, len(target_names)):
patch = mpatches.Patch(color=color_list_bold[i], label=target_names[i])
legend_handles.append(patch)
subplot.legend(loc=0, handles=legend_handles)
def plot_class_regions_for_classifier(clf, X, y, X_test=None, y_test=None, title=None, target_names = None, plot_decision_regions = True):
numClasses = numpy.amax(y) + 1
color_list_light = ['#FFFFAA', '#EFEFEF', '#AAFFAA', '#AAAAFF']
color_list_bold = ['#EEEE00', '#000000', '#00CC00', '#0000CC']
cmap_light = ListedColormap(color_list_light[0:numClasses])
cmap_bold = ListedColormap(color_list_bold[0:numClasses])
h = 0.03
k = 0.5
x_plot_adjust = 0.1
y_plot_adjust = 0.1
plot_symbol_size = 50
x_min = X[:, 0].min()
x_max = X[:, 0].max()
y_min = X[:, 1].min()
y_max = X[:, 1].max()
x2, y2 = numpy.meshgrid(numpy.arange(x_min-k, x_max+k, h), numpy.arange(y_min-k, y_max+k, h))
P = clf.predict(numpy.c_[x2.ravel(), y2.ravel()])
P = P.reshape(x2.shape)
plt.figure()
if plot_decision_regions:
plt.contourf(x2, y2, P, cmap=cmap_light, alpha = 0.8)
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, s=plot_symbol_size, edgecolor = 'black')
plt.xlim(x_min - x_plot_adjust, x_max + x_plot_adjust)
plt.ylim(y_min - y_plot_adjust, y_max + y_plot_adjust)
if (X_test is not None):
plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cmap_bold, s=plot_symbol_size, marker='^', edgecolor = 'black')
train_score = clf.score(X, y)
test_score = clf.score(X_test, y_test)
title = title + "\nTrain score = {:.2f}, Test score = {:.2f}".format(train_score, test_score)
if (target_names is not None):
legend_handles = []
for i in range(0, len(target_names)):
patch = mpatches.Patch(color=color_list_bold[i], label=target_names[i])
legend_handles.append(patch)
plt.legend(loc=0, handles=legend_handles)
if (title is not None):
plt.title(title)
plt.show()
def plot_fruit_knn(X, y, n_neighbors, weights):
X_mat = X[['height', 'width']].as_matrix()
y_mat = y.as_matrix()
# Create color maps
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF','#AFAFAF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF','#AFAFAF'])
clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
clf.fit(X_mat, y_mat)
# Plot the decision boundary by assigning a color in the color map
# to each mesh point.
mesh_step_size = .01 # step size in the mesh
plot_symbol_size = 50
x_min, x_max = X_mat[:, 0].min() - 1, X_mat[:, 0].max() + 1
y_min, y_max = X_mat[:, 1].min() - 1, X_mat[:, 1].max() + 1
xx, yy = numpy.meshgrid(numpy.arange(x_min, x_max, mesh_step_size),
numpy.arange(y_min, y_max, mesh_step_size))
Z = clf.predict(numpy.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure()
plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
# Plot training points
plt.scatter(X_mat[:, 0], X_mat[:, 1], s=plot_symbol_size, c=y, cmap=cmap_bold, edgecolor = 'black')
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
patch0 = mpatches.Patch(color='#FF0000', label='apple')
patch1 = mpatches.Patch(color='#00FF00', label='mandarin')
patch2 = mpatches.Patch(color='#0000FF', label='orange')
patch3 = mpatches.Patch(color='#AFAFAF', label='lemon')
plt.legend(handles=[patch0, patch1, patch2, patch3])
plt.xlabel('height (cm)')
plt.ylabel('width (cm)')
plt.show()
def plot_two_class_knn(X, y, n_neighbors, weights, X_test, y_test):
X_mat = X
y_mat = y
# Create color maps
cmap_light = ListedColormap(['#FFFFAA', '#AAFFAA', '#AAAAFF','#EFEFEF'])
cmap_bold = ListedColormap(['#FFFF00', '#00FF00', '#0000FF','#000000'])
clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
clf.fit(X_mat, y_mat)
# Plot the decision boundary by assigning a color in the color map
# to each mesh point.
mesh_step_size = .01 # step size in the mesh
plot_symbol_size = 50
x_min, x_max = X_mat[:, 0].min() - 1, X_mat[:, 0].max() + 1
y_min, y_max = X_mat[:, 1].min() - 1, X_mat[:, 1].max() + 1
xx, yy = numpy.meshgrid(numpy.arange(x_min, x_max, mesh_step_size),
numpy.arange(y_min, y_max, mesh_step_size))
Z = clf.predict(numpy.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure()
plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
# Plot training points
plt.scatter(X_mat[:, 0], X_mat[:, 1], s=plot_symbol_size, c=y, cmap=cmap_bold, edgecolor = 'black')
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
title = "Neighbors = {}".format(n_neighbors)
if (X_test is not None):
train_score = clf.score(X_mat, y_mat)
test_score = clf.score(X_test, y_test)
title = title + "\nTrain score = {:.2f}, Test score = {:.2f}".format(train_score, test_score)
patch0 = mpatches.Patch(color='#FFFF00', label='class 0')
patch1 = mpatches.Patch(color='#000000', label='class 1')
plt.legend(handles=[patch0, patch1])
plt.xlabel('Feature 0')
plt.ylabel('Feature 1')
plt.title(title)
plt.show()