diff --git a/mglearn/__init__.py b/mglearn/__init__.py
index 8b2cb09..38be245 100644
--- a/mglearn/__init__.py
+++ b/mglearn/__init__.py
@@ -4,4 +4,6 @@
from .tools import discrete_scatter
from .plot_helpers import ReBl
+__version__ = "0.2.0"
+
__all__ = ['tools', 'plots', 'cm3', 'cm2', 'discrete_scatter', 'ReBl']
diff --git a/mglearn/datasets.py b/mglearn/datasets.py
index 961a799..6d29925 100644
--- a/mglearn/datasets.py
+++ b/mglearn/datasets.py
@@ -2,18 +2,18 @@
import pandas as pd
import os
from scipy import signal
-from sklearn.datasets import load_boston
from sklearn.preprocessing import MinMaxScaler, PolynomialFeatures
from sklearn.datasets import make_blobs
+from sklearn.utils import Bunch
-DATA_PATH = os.path.join(os.path.dirname(__file__), "..", "data")
+DATA_PATH = os.path.join(os.path.dirname(__file__), "data")
def make_forge():
# a carefully hand-designed dataset lol
X, y = make_blobs(centers=2, random_state=4, n_samples=30)
y[np.array([7, 27])] = 0
- mask = np.ones(len(X), dtype=np.bool)
+ mask = np.ones(len(X), dtype=bool)
mask[np.array([0, 1, 5, 26])] = 0
X, y = X[mask], y[mask]
return X, y
@@ -27,6 +27,19 @@ def make_wave(n_samples=100):
return x.reshape(-1, 1), y
+def load_boston():
+ try:
+ from sklearn.datasets import load_boston
+ return load_boston()
+ except ImportError:
+ pass
+ data_url = "http://lib.stat.cmu.edu/datasets/boston"
+ raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None)
+ data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
+ target = raw_df.values[1::2, 2]
+ return Bunch(data=data, target=target)
+
+
def load_extended_boston():
boston = load_boston()
X = boston.data
diff --git a/mglearn/plot_2d_separator.py b/mglearn/plot_2d_separator.py
index 25b549a..943d216 100644
--- a/mglearn/plot_2d_separator.py
+++ b/mglearn/plot_2d_separator.py
@@ -2,34 +2,6 @@
import matplotlib.pyplot as plt
from .plot_helpers import cm2, cm3, discrete_scatter
-def _call_classifier_chunked(classifier_pred_or_decide, X):
- # The chunk_size is used to chunk the large arrays to work with x86
- # memory models that are restricted to < 2 GB in memory allocation. The
- # chunk_size value used here is based on a measurement with the
- # MLPClassifier using the following parameters:
- # MLPClassifier(solver='lbfgs', random_state=0,
- # hidden_layer_sizes=[1000,1000,1000])
- # by reducing the value it is possible to trade in time for memory.
- # It is possible to chunk the array as the calculations are independent of
- # each other.
- # Note: an intermittent version made a distinction between
- # 32- and 64 bit architectures avoiding the chunking. Testing revealed
- # that even on 64 bit architectures the chunking increases the
- # performance by a factor of 3-5, largely due to the avoidance of memory
- # swapping.
- chunk_size = 10000
-
- # We use a list to collect all result chunks
- Y_result_chunks = []
-
- # Call the classifier in chunks.
- for x_chunk in np.array_split(X, np.arange(chunk_size, X.shape[0],
- chunk_size, dtype=np.int32),
- axis=0):
- Y_result_chunks.append(classifier_pred_or_decide(x_chunk))
-
- return np.concatenate(Y_result_chunks)
-
def plot_2d_classification(classifier, X, fill=False, ax=None, eps=None,
alpha=1, cm=cm3):
@@ -110,16 +82,14 @@ def plot_2d_separator(classifier, X, fill=False, ax=None, eps=None, alpha=1,
X1, X2 = np.meshgrid(xx, yy)
X_grid = np.c_[X1.ravel(), X2.ravel()]
- if hasattr(classifier, "decision_function"):
- decision_values = _call_classifier_chunked(classifier.decision_function,
- X_grid)
+ try:
+ decision_values = classifier.decision_function(X_grid)
levels = [0] if threshold is None else [threshold]
fill_levels = [decision_values.min()] + levels + [
decision_values.max()]
- else:
+ except AttributeError:
# no decision_function
- decision_values = _call_classifier_chunked(classifier.predict_proba,
- X_grid)[:, 1]
+ decision_values = classifier.predict_proba(X_grid)[:, 1]
levels = [.5] if threshold is None else [threshold]
fill_levels = [0] + levels + [1]
if fill:
@@ -133,14 +103,4 @@ def plot_2d_separator(classifier, X, fill=False, ax=None, eps=None, alpha=1,
ax.set_xlim(x_min, x_max)
ax.set_ylim(y_min, y_max)
ax.set_xticks(())
- ax.set_yticks(())
-
-
-if __name__ == '__main__':
- from sklearn.datasets import make_blobs
- from sklearn.linear_model import LogisticRegression
- X, y = make_blobs(centers=2, random_state=42)
- clf = LogisticRegression(solver='lbfgs').fit(X, y)
- plot_2d_separator(clf, X, fill=True)
- discrete_scatter(X[:, 0], X[:, 1], y)
- plt.show()
+ ax.set_yticks(())
\ No newline at end of file
diff --git a/mglearn/plot_grid_search.py b/mglearn/plot_grid_search.py
index fe805d5..884bae7 100644
--- a/mglearn/plot_grid_search.py
+++ b/mglearn/plot_grid_search.py
@@ -33,9 +33,8 @@ def plot_cross_val_selection():
marker_best, = plt.plot(i, row.mean_test_score, 'o', c='red',
fillstyle="none", alpha=1, markersize=20,
markeredgewidth=3)
-
plt.xticks(range(len(results)), [str(x).strip("{}").replace("'", "") for x
- in grid_search.cv_results_['params']],
+ in results['params']],
rotation=90)
plt.ylabel("Validation accuracy")
plt.xlabel("Parameter settings")
diff --git a/mglearn/plot_kneighbors_regularization.py b/mglearn/plot_kneighbors_regularization.py
index 825dacf..71b1e94 100644
--- a/mglearn/plot_kneighbors_regularization.py
+++ b/mglearn/plot_kneighbors_regularization.py
@@ -22,9 +22,4 @@ def plot_kneighbors_regularization():
ax.plot(x_test, kneighbor_regression.predict(x_test[:, np.newaxis]),
label="prediction")
ax.legend()
- ax.set_title("n_neighbors = %d" % n_neighbors)
-
-
-if __name__ == "__main__":
- plot_kneighbors_regularization()
- plt.show()
+ ax.set_title("n_neighbors = %d" % n_neighbors)
\ No newline at end of file
diff --git a/mglearn/plot_linear_svc_regularization.py b/mglearn/plot_linear_svc_regularization.py
index 5f8e0dc..3ee92b9 100644
--- a/mglearn/plot_linear_svc_regularization.py
+++ b/mglearn/plot_linear_svc_regularization.py
@@ -31,7 +31,3 @@ def plot_linear_svc_regularization():
ax.set_yticks(())
ax.set_title("C = %f" % C)
axes[0].legend(loc="best")
-
-if __name__ == "__main__":
- plot_linear_svc_regularization()
- plt.show()
diff --git a/mglearn/plot_nmf.py b/mglearn/plot_nmf.py
index dbea642..2651f95 100644
--- a/mglearn/plot_nmf.py
+++ b/mglearn/plot_nmf.py
@@ -4,7 +4,11 @@
from joblib import Memory
-memory = Memory(location="cache")
+try:
+ memory = Memory(cachedir="cache")
+except TypeError:
+ # joblib.Memory changed its API in 0.12
+ memory = Memory(location="cache", verbose=0)
def plot_nmf_illustration():
diff --git a/mglearn/plot_pca.py b/mglearn/plot_pca.py
index 25ce634..65a9ded 100644
--- a/mglearn/plot_pca.py
+++ b/mglearn/plot_pca.py
@@ -4,8 +4,11 @@
from joblib import Memory
-memory = Memory(location="cache")
-
+try:
+ memory = Memory(cachedir="cache")
+except TypeError:
+ # joblib.Memory changed its API in 0.12
+ memory = Memory(location="cache", verbose=0)
def plot_pca_illustration():
rnd = np.random.RandomState(5)
diff --git a/mglearn/plots.py b/mglearn/plots.py
index 4dcb507..b275a3f 100644
--- a/mglearn/plots.py
+++ b/mglearn/plots.py
@@ -28,6 +28,7 @@
plot_decision_threshold)
from .plot_dbscan import plot_dbscan
from .plot_ridge import plot_ridge_n_samples
+from .plot_kneighbors_regularization import plot_kneighbors_regularization
__all__ = ['plot_linear_svc_regularization',
"plot_animal_tree", "plot_tree_progressive",
@@ -65,5 +66,6 @@
'plot_binary_confusion_matrix',
'plot_decision_threshold',
'plot_dbscan',
- 'plot_ridge_n_samples'
+ 'plot_ridge_n_samples',
+ 'plot_kneighbors_regularization'
]
diff --git a/mglearn/tools.py b/mglearn/tools.py
index b67686e..f6b7dd1 100644
--- a/mglearn/tools.py
+++ b/mglearn/tools.py
@@ -85,7 +85,7 @@ def make_handcrafted_dataset():
# a carefully hand-designed dataset lol
X, y = make_blobs(centers=2, random_state=4, n_samples=30)
y[np.array([7, 27])] = 0
- mask = np.ones(len(X), dtype=np.bool)
+ mask = np.ones(len(X), dtype=bool)
mask[np.array([0, 1, 5, 26])] = 0
X, y = X[mask], y[mask]
return X, y