Python plot_ts_slider_highligther示例

示例#1

文件： 8_srrl_directional_features.py 项目： benbenboben/test

vis = visualize.Visualizer()
vis.add_line_ser(test.df['GHI'], 'GHI')
vis.add_line_ser(test.df['Clearsky GHI pvlib'], 'GHI_cs')
vis.add_circle_ser(test.df[ml_clear & ~nsrdb_clear]['GHI'], 'ML clear only')
vis.add_circle_ser(test.df[~ml_clear & nsrdb_clear]['GHI'], 'NSRDB clear only')
vis.add_circle_ser(test.df[ml_clear & nsrdb_clear]['GHI'], 'Both clear')
vis.show()


# In[51]:


probas = clf.predict_proba(test.df[feature_cols].values)
test.df['probas'] = 0
test.df['probas'] = probas[:, 1]
visualize.plot_ts_slider_highligther(test.df, prob='probas')

## 15 min freq ground dataground = cs_detection.ClearskyDetection.read_pickle('abq_ground_1.pkl.gz')
ground.df.index = ground.df.index.tz_convert('MST')
test = cs_detection.ClearskyDetection(ground.df)test.trim_dates('10-01-2015', '10-17-2015')test.time_from_solar_noon('Clearsky GHI pvlib', 'tfn')test.df = test.df[test.df.index.minute % 15 == 0]pred = test.iter_predict_daily(feature_cols, 'GHI', 'Clearsky GHI pvlib', clf, 5, multiproc=True, by_day=True).astype(bool)train2 = cs_detection.ClearskyDetection(train.df)
train2.trim_dates('10-01-2015', '10-17-2015')
train2.df = train2.df.reindex(pd.date_range(start=train2.df.index[0], end=train2.df.index[-1], freq='15min'))
train2.df['sky_status'] = train2.df['sky_status'].fillna(False)nsrdb_clear = train2.df['sky_status']
ml_clear = test.df['sky_status iter']
vis = visualize.Visualizer()
vis.add_line_ser(test.df['GHI'], 'GHI')
vis.add_line_ser(test.df['Clearsky GHI pvlib'], 'GHI_cs')
vis.add_circle_ser(test.df[ml_clear & ~nsrdb_clear]['GHI'], 'ML clear only')
vis.add_circle_ser(test.df[~ml_clear & nsrdb_clear]['GHI'], 'NSRDB clear only')
vis.add_circle_ser(test.df[ml_clear & nsrdb_clear]['GHI'], 'Both clear')
vis.show()probas = clf.predict_proba(test.df[feature_cols].values)

示例#2

文件： 6_ml_exploration_combined_training_new_features_new_clf-Copy1.py 项目： benbenboben/test

    test.df[(test.df['sky_status pvlib'] == 1) & (~pred)]['GHI'],
    'PVLib clear only')
vis.add_circle_ser(test.df[(test.df['sky_status pvlib'] == 1) & (pred)]['GHI'],
                   'ML+PVLib clear only')
# vis.add_line_ser(test.df['abs_ideal_ratio_diff'] * 100)

# In[118]:

vis.show()

# In[119]:

probas = clf.predict_proba(test.df[feature_cols].values)
test.df['probas'] = 0
test.df['probas'] = probas[:, 1]
visualize.plot_ts_slider_highligther(test.df, prob='probas')

# In[120]:

ground = cs_detection.ClearskyDetection.read_pickle('srrl_ground_1.pkl.gz')

# In[121]:

ground.df.index = ground.df.index.tz_convert('MST')

# In[122]:

ground.trim_dates('10-01-2011', '10-08-2011')

# In[123]:

示例#3

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


# In[104]:


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

test2 = cs_detection.ClearskyDetection(test.df)
test2.trim_dates('10-01-2015', None)
probas = clf.predict_proba(test2.df[feature_cols].values)
test2.df['probas'] = 0
test2.df['probas'] = probas[:, 1]

visualize.plot_ts_slider_highligther(test2.df, prob='probas')
# # Train on all NSRDB data, test various freq of ground data

# In[105]:


train = cs_detection.ClearskyDetection(nsrdb.df)
train.scale_model('GHI', 'Clearsky GHI pvlib', 'sky_status')
utils.calc_all_window_metrics(train.df, 3, meas_col='GHI', model_col='Clearsky GHI pvlib', overwrite=True)
clf.fit(train.df[feature_cols].values, train.df[target_cols].values.flatten())


# In[106]:


bar = go.Bar(x=feature_cols, y=clf.feature_importances_)