Python plot_confusion_matrix2 Exemples

Exemple #1

Fichier : 10_clean_feature_selection_snl.py Projet : benbenboben/test

# In[10]:


print(metrics.f1_score(test_obj.df['sky_status'], pred))


# In[11]:


cm = metrics.confusion_matrix(test_obj.df['sky_status'], pred)


# In[12]:


visualize.plot_confusion_matrix2(cm, ('cloudy', 'clear'))


# In[13]:


fig, ax = plt.subplots(figsize=(12, 8))

_ = ax.bar(range(len(clf.feature_importances_)), clf.feature_importances_)
_ = ax.set_xticks(range(len(clf.feature_importances_)))
_ = ax.set_xticklabels(test_obj.features_, rotation=45)

_ = ax.set_ylabel('Importance')
_ = ax.set_xlabel('Feature')

_ = fig.tight_layout()

Exemple #2

Fichier : 9_data_cleaning_remove_bad_points_srrl.py Projet : benbenboben/test

# In[41]:

print(metrics.accuracy_score(test_obj.df['sky_status'], pred))

# In[42]:

print(metrics.recall_score(test_obj.df['sky_status'], pred))

# In[43]:

cm = metrics.confusion_matrix(test_obj.df['sky_status'], pred)

# In[44]:

visualize.plot_confusion_matrix2(cm, ('cloudy', 'clear'))

# In[45]:

fig, ax = plt.subplots(figsize=(12, 8))

_ = ax.bar(range(len(clf.feature_importances_)), clf.feature_importances_)
_ = ax.set_xticks(range(len(clf.feature_importances_)))
_ = ax.set_xticklabels(test_obj.features_, rotation=45)

_ = ax.set_ylabel('Importance')
_ = ax.set_xlabel('Feature')

_ = fig.tight_layout()

# In[46]:

Exemple #3

Fichier : 9_data_cleaning_initial_analysis.py Projet : benbenboben/test

# In[220]:


print(metrics.recall_score(test_obj.df['sky_status'], pred))


# In[221]:


cm = metrics.confusion_matrix(test_obj.df['sky_status'], pred)


# In[222]:


visualize.plot_confusion_matrix2(cm, ('cloudy', 'clear'))


# In[223]:


fig, ax = plt.subplots(figsize=(12, 8))

_ = ax.bar(range(len(clf.feature_importances_)), clf.feature_importances_)
_ = ax.set_xticks(range(len(clf.feature_importances_)))
_ = ax.set_xticklabels(test_obj.features_, rotation=45)

_ = ax.set_ylabel('Importance')
_ = ax.set_xlabel('Feature')

_ = fig.tight_layout()

Exemple #4

Fichier : 9_data_cleaning_remove_bad_points_snl-Copy1.py Projet : benbenboben/test

get_ipython().magic("config InlineBackend.figure_format = 'retina'")

matplotlib.rcParams.update({'font.size': 16})

import warnings
warnings.filterwarnings(action='ignore')


plt.close('all')

# Train on default data# nsrdb = pd.read_pickle('abq_nsrdb_1.pkl.gz')
detect_obj = cs_detection.ClearskyDetection.read_pickle('abq_nsrdb_1.pkl.gz', 'GHI', 'Clearsky GHI pvlib', 'sky_status')
detect_obj.df.index = detect_obj.df.index.tz_convert('MST')train_obj = cs_detection.ClearskyDetection(detect_obj.df, 'GHI', 'Clearsky GHI pvlib', 'sky_status')
train_obj.trim_dates(None, '01-01-2015')
test_obj = cs_detection.ClearskyDetection(detect_obj.df, 'GHI', 'Clearsky GHI pvlib', 'sky_status')
test_obj.trim_dates('01-01-2015', None)clf = ensemble.RandomForestClassifier(n_jobs=-1, n_estimators=32, max_depth=10, random_state=42)clf = train_obj.fit_model(clf)pred = test_obj.predict(clf)print(metrics.accuracy_score(test_obj.df['sky_status'], pred))print(metrics.recall_score(test_obj.df['sky_status'], pred))cm = metrics.confusion_matrix(test_obj.df['sky_status'], pred)visualize.plot_confusion_matrix2(cm, ('cloudy', 'clear'))fig, ax = plt.subplots(figsize=(12, 8))

_ = ax.bar(range(len(clf.feature_importances_)), clf.feature_importances_)
_ = ax.set_xticks(range(len(clf.feature_importances_)))
_ = ax.set_xticklabels(test_obj.features_, rotation=45)

_ = ax.set_ylabel('Importance')
_ = ax.set_xlabel('Feature')

_ = fig.tight_layout()fig, ax = plt.subplots(figsize=(12, 8))

nsrdb_mask = test_obj.df['sky_status'].values

ax.plot(test_obj.df.index, test_obj.df['GHI'], label='GHI', alpha=.5)
ax.plot(test_obj.df.index, test_obj.df['Clearsky GHI pvlib'], label='GHIcs', alpha=.5)
ax.scatter(test_obj.df[pred & ~nsrdb_mask].index, test_obj.df[pred & ~nsrdb_mask]['GHI'], label='RF only', zorder=10, s=10)