desc = Div(text="""
<h2 style="font-family="Arial">
Select the features to be included in the Ridge Regression Model
</h2>
<p><a href="http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html#sklearn.linear_model.Ridge" target="_blank">Click here </a>for more information on the parameters </p>
""",
width=1100)
df = pd.read_csv(datasetname)
y = df[df.columns[:1]].values.ravel()
df1 = df.drop(df.columns[:1], axis=1)
target = Paragraph(text='', name='target')
target.text = "Target feature is " + str(df.columns[:1].tolist())
features = MultiSelect(title="Features", options=df.columns[1:].tolist())
kfold = Slider(start=2, end=10, value=5, step=1, title="No of folds")
fit_intercept = Select(title="Fit_intercept:",
value="True",
options=["True", "False"])
normalize = Select(title="Normalize:",
value="False",
options=["True", "False"])
copy_X = Select(title="Copy_X:", value="True", options=["True", "False"])
solver = Select(
title="Solver:",
value="auto",
options=["auto", "svd", "cholesky", "lsqr", "sparse_cg", "sag", "saga"])
max_depth = Slider(start=0, end=50, value=10, step=1, title="Max_Depth")
n_estimators = Slider(start=0,
end=30,
value=10,
step=1,
title="No of estimators:")
bootstrap = Select(title="Bootstrap:", value="True", options=["True", "False"])
oob_score = Select(title="oob_score:",
value="False",
options=["True", "False"])
warm_start = Select(title="Warm_start:",
value="False",
options=["True", "False"])
target = Paragraph(text='', name='target')
target.text = "Target feature is " + str(df.columns[:1].tolist())
stats = Paragraph(text='', width=1000, name='Selected Features:')
y = df[df.columns[:1]].values.ravel()
df1 = df.drop(df.columns[:1], axis=1)
selector = SelectKBest(chi2, k=5).fit(df1, y)
X_new = selector.transform(df1)
mask = selector.get_support() #list of booleans
new_features = [] # The list of your K best features
for bool, feature in zip(mask, df.columns[1:].tolist()):
if bool:
new_features.append(feature)
#print(new_features)
value=str(DEFAULT_AIR_THICKNESS))
air_pressure_input = TextInput(title='air pressure',
value=str(DEFAULT_AIR_PRESSURE))
air_temperature_input = TextInput(title='air temperature',
value=str(DEFAULT_AIR_TEMPERATURE))
detector_material_input = AutocompleteInput(title='Detector',
value=DEFAULT_DETECTOR_MATERIAL)
detector_material_input.completions = all_materials
detector_thickness_input = TextInput(title='thickness',
value=str(DEFAULT_DETECTOR_THICKNESS))
detector_density_input = TextInput(title='density',
value=str(DEFAULT_DETECTOR_DENSITY))
p = Paragraph(text="", width=500)
p.text = f"Running roentgen version {roentgen.__version__}"
columns = [
TableColumn(field="x", title="energy [keV]"),
TableColumn(field="y", title="Percent"),
]
data_table = DataTable(source=source, columns=columns, width=400, height=700)
download_button = Button(label="Download", button_type="success")
download_button.js_on_event(
ButtonClick,
CustomJS(args=dict(source=source),
code=open(join(dirname(__file__), "download.js")).read()))
def convert_air_pressure(value, current_unit, new_unit):
#df1 = pd.DataFrame(df, columns=columns)
#y = df['churn']
y = df[df.columns[:1]].values.ravel()
df1 = df.drop(df.columns[:1], axis=1)
selector = SelectKBest(chi2, k=5).fit(df1, y)
X_new = selector.transform(df1)
mask = selector.get_support() #list of booleans
new_features = [] # The list of your K best features
for bool, feature in zip(mask, df.columns[1:].tolist()):
if bool:
new_features.append(feature)
#print(new_features)
stats.text = str(new_features)
x_train_original, x_test_original, y_train_original, y_test_original = train_test_split(
X_new, y, test_size=0.25)
#For standardizing data
#clf = svm.LinearSVC(random_state=0)
clf = GaussianProcessClassifier()
clf.fit(x_train_original, y_train_original)
predictions = clf.predict(x_test_original)
#print("Accuracy =", accuracy_score(y_test_original,predictions))
#print(np.unique(predictions))
tn, fp, fn, tp = confusion_matrix(y_test_original, predictions).ravel()
fruits = ['True Positive', 'False Positive', 'True Negative', 'False Negative']
#fruits = [tp, fp, tn, fn]
+ css,
width=1024,
height=39)
p1 = Div(text="""<font size="+2", color="#154696"><b>Settings</b></font>""",
width=600,
height=23)
p2 = Div(text="""<font size="+2", color="#154696"><b>Results </b></font>""",
width=600,
height=33)
error = Paragraph()
calculation_time = Paragraph()
total_BED = Paragraph()
model_type = Paragraph()
num_of_calls = Paragraph()
info_data_uploaded = Paragraph()
info_data_uploaded.text = default_file_BED + " loaded."
rdn_btn_title = Div(
text="""<font size="-0.5">Select the type of model: </font>""",
width=600,
height=15)
in_model_rdn_btn = RadioButtonGroup(
labels=["Linear", "Heuristic", "Automatic"], active=2)
in_model_rdn_btn.callback = CustomJS(
args=dict(radioButton=in_model_rdn_btn),
code=bgui_js_handlers.in_model_rdn_btn_callback_code)
in_model_rdn_btn.on_change("active", selected_model_changed)
in_min_time = ColumnDataSource(data=dict(val=[MIN_LP_TIME]))
in_time_slider = Slider(start=MIN_LP_TIME,
end=MAX_LP_TIME,
value=10,
""",
width=1100)
#obj = client.get_object(Bucket='my-bucket', Key='churn.csv')
#df = pd.read_csv(obj['Body'])
#df = pd.read_csv('/Users/adilkhan/Documents/CS Fall 16/CS297/Bokeh-Demo/EmbedWebsite/cancer.csv')
df = pd.read_csv(datasetname)
y = df[df.columns[:1]].values.ravel()
df1 = df.drop(df.columns[:1], axis=1)
features = MultiSelect(title="Features", options=df.columns[1:].tolist())
target = Paragraph(text='', name='target')
target.text = "Target feature is " + str(df.columns[:1].tolist())
stats = Paragraph(text='', width=1000, name='Selected Features:')
y = df[df.columns[:1]].values.ravel()
df1 = df.drop(df.columns[:1], axis=1)
selector = SelectKBest(chi2, k=5).fit(df1, y)
X_new = selector.transform(df1)
mask = selector.get_support() #list of booleans
new_features = [] # The list of your K best features
for bool, feature in zip(mask, df.columns[1:].tolist()):
if bool:
new_features.append(feature)
#print(new_features)
features.value = new_features
# widget documentation:
# http://bokeh.pydata.org/en/latest/docs/user_guide/interaction/widgets.html#select
# 0. imports
from bokeh.io import curdoc
from bokeh.layouts import column
from bokeh.models.widgets import TextInput, Button, Paragraph, Select
# 1. create some widgets
title = Paragraph()
title.text = 'Name Selector'
button = Button(label="Say HI")
#input = TextInput(value="Bokeh")
output = Paragraph()
select = Select(
title='Name:',
value='select',
options=['Jordan', 'Matthew', 'Rebecca', 'Austin', 'Josh', 'Laura'])
# 2. add a callback to a widget
def update():
#output.text = "Hello, " + input.value
output.text = "Hello, " + select.value
button.on_click(update)
# 3. create a layout for everything
#layout = column(button, input, output)
layout = column(button, select, output)
ctl_num_nodes = Slider(title="Num. nodes", value=rml.NUM_NODES,
start=1, end=500, step=1)
ctl_hidden = Slider(title="Num. hidden layers", value=rml.NUM_HIDDEN_LAYERS,
start=1, end=500, step=1)
ctl_inputs = widgetbox(ctl_model_title, ctl_model, ctl_title, ctl_feat_reduce, ctl_est, ctl_pct_test, ctl_kernel,
ctl_c_val, ctl_neighbors, ctl_num_nodes, ctl_hidden)
disp_features = Paragraph(text="")
disp_score = Paragraph(text="Score: --")
# Data Sources and Initialization
d_data = rml.preprocess(rml.read_file("daylio_export.csv"))
d_features = rml.extract_features(d_data)
if ctl_feat_reduce.active:
d_features = rml.feature_select(d_features, d_data["mood"])
x, y = range(len(d_data)), d_data["mood"]
disp_features.text = ", ".join([c.title() for c in d_features.keys()])
source_data = ColumnDataSource(data=dict(x=x, y=y, timestamp=d_data["date"] + ", " + d_data["year"].apply(str)))
pred_data = ColumnDataSource(
data=dict(x=x, y=[0, ] * len(y), timestamp=d_data["date"] + ", " + d_data["year"].apply(str)))
plot_mood_scatter.scatter('x', 'y', source=source_data)
xrange_data = Range1d(bounds=[None, None], start=0, end=len(y))
yrange_data = Range1d(bounds=[None, None], start=Y_MIN, end=Y_MAX)
plot_mood_scatter.x_range = xrange_data
plot_mood_scatter.y_range = yrange_data
# Set up bar graph
source_bars = ColumnDataSource(dict(y=d_data["mood"].value_counts().index, right=d_data["mood"].value_counts()))
pred_line = ColumnDataSource(dict(y=d_data["mood"].value_counts().index, x=[0, ] * len(d_data["mood"].value_counts())))
def beliebtheitsermittler(df,actorList, productionList , directorList, writersList, genreList,df_merged, ratingQuantile ):
text = "Füllen Sie die Felder aus und drücken auf den Start-Button um den Film zu bewerten."
actorList = list(set(actorList))
productionList = list(set(productionList))
directorList = list(set(directorList))
writersList = list(set(writersList))
genreList = list(set(genreList))
output_notebook()
# Set up widgets
actor1 = AutocompleteInput(completions=actorList, placeholder = 'Required',title='Schauspieler 1')
actor2 = AutocompleteInput(completions=actorList, placeholder = 'Required', title='Schauspieler 2')
actor3 = AutocompleteInput(completions=actorList, placeholder = 'Required', title='Schauspieler 3')
actor4 = AutocompleteInput(completions=actorList, placeholder = 'Required', title='Schauspieler 4')
production = AutocompleteInput(completions=productionList, placeholder = 'Required', title='Production')
director = AutocompleteInput(completions= directorList, placeholder = 'Required', title='Regie')
autor = AutocompleteInput(completions= writersList, placeholder = 'Required', title='Autor')
genre = Select(title = 'Auswahl des Hauptgenre',height = 50,value = 'Comedy', options = genreList)
select = Select(title = 'Auswahl der Metric',height = 50,value = 'Metascore', options = ['Metascore', 'imdbRating', 'TomatoRating'])
answer = Button(height = 100, width = 600,disabled = True,margin = [10,10,10,10],background = 'white', label = text)
button = Button(margin = [23,0,0,200], width = 100, button_type = 'primary', label = 'Start')
paragraph2 = Paragraph(margin = [40,0,0,10])
paragraph2.text = "Ergebnis:"
def doOnClick():
metric = select.value
columns = df_merged.columns.tolist()
columns.remove('Category')
if (actor1.value in actorList) and (actor2.value in actorList) and (actor3.value in actorList) and (actor4.value in actorList) and (production.value in productionList) and (director.value in directorList) and (autor.value in writersList):
filename = 'models/'+ metric + '_model.sav'
model = pickle.load(open(filename, 'rb'))
d = pd.DataFrame(0,index=np.arange(1), columns=columns)
d['Actors_' + actor1.value] = 1
d['Actors_' + actor2.value] = 1
d['Actors_' + actor3.value] = 1
d['Actors_' + actor4.value] = 1
d['Genre_' + genre.value] = 1
d['Director_' + director.value] = 1
d['Writer_' + autor.value] = 1
d['Production__'+ production.value] = 1
result = model.predict(d)[0]
if ('Gut' in result):
answer.background = 'green'
elif('Schlecht' in result):
answer.background = 'red'
else:
answer.background = 'yellow'
answer.label = result
else:
answer.label = text
answer.background = 'white'
button.on_click(doOnClick)
layout = [row(widgetbox(actor1 , actor2, actor3, actor4, genre),
widgetbox( production, director,autor, select, button ))]
def modify_doc(doc):
doc.add_root(row(layout))
doc.add_root(column(widgetbox(paragraph2,answer)))
handler = FunctionHandler(modify_doc)
app = Application(handler)
show(app)
