I'm trying to build a simple app with bokeh where the histogram updates when a new variable is selected in the dropdown menu. The histogram indeed updates but not the x-axis (the categories). I read similar issues on different forums but none of the answers solved my issues.
Here is my code:
def modify_doc(doc):
def make_dataset(var_name):
CountDf=pd.DataFrame(TrainData[var_name].value_counts()).reset_index()
CountDf.columns=['CATEGORY','COUNT']
#return(CountDf)
return(ColumnDataSource(CountDf))
def make_plot(src):
#p=figure(x_range=list(src.data['CATEGORY']))
p=figure(x_range=FactorRange(factors=list(src.data['CATEGORY'])))
p.x_range.factors = src.data['CATEGORY']
p.vbar(x='CATEGORY', top='COUNT', width=0.9, source=src)
p.xaxis.major_label_orientation = math.pi/2
return(p)
# Update function takes three default parameters
def update(attr, old, new):
new_src = make_dataset(new)
src.data.update(new_src.data)
p.x_range.factors = src.data['CATEGORY']
var_selection = Dropdown(label='Variable to plot:',menu=CatVarLst, value=CatVarLst[0])
var_selection.on_change('value',update)
controls = WidgetBox(var_selection)
initial_var=CatVarLst[0]
src=make_dataset(initial_var)
p = make_plot(src)
layout = column(controls, p)
doc.add_root(layout)
Thank you for your help!
Related
I've already trained several models for a binary classification problem, basing my election on F-Score and AUC. The code used has been the following:
svm = StandardScaler()
svm.fit(feat_train)
feat_train_std = svm.transform(feat_train)
feat_test_std = svm.transform(feat_test)
model_10= BalancedBaggingClassifier(base_estimator=SVC(C=1.0, random_state=1, kernel='linear'),
sampling_strategy='auto',
replacement=False,
random_state=0)
model_10.fit(feat_train_std, target_train)
pred_target_10 = model_10.predict(feat_test)
mostrar_resultados(target_test, pred_target_10)
pred_target_10 = model_10.predict_proba(feat_test)[:, 1]
average_precision_10 = average_precision_score(target_test, pred_target_10)
precision_10, recall_10, thresholds = precision_recall_curve(target_test, pred_target_10)
auc_precision_recall_10 = auc(recall_10, precision_10)
disp_10 = plot_precision_recall_curve(model_10, feat_test, target_test)
disp_10.ax_.set_title('Binary class Precision-Recall curve: '
'AUC={0:0.2f}'.format(auc_precision_recall_10))
Afterwards, I load the model as follows:
modelo_pickle = 'modelo_pickle.pkl'
joblib.dump(model_10,modelo_pickle)
loaded_model = joblib.load(modelo_pickle)
Then, the aim is to load a new dataset, which columns are the same as the model's variables, and make a prediction for each line:
lista_x=x.to_numpy().tolist()
resultados=[]
for i in lista_x:
pred = loaded_model.predict([i])
resultados.append(pred)
print(resultados)
However, every single result is equal to 1, which does not make any sense. Would anyone tell me what am I missing, please?
Thank you in advance.
Regards,
Previously described.
I need to create a heatmap, before plotting heatmap, I need to download a lot of data from database which take time like 5 minutes, I'd like to show a progress bar when downloading data from oracle database to let me know if it is in progress of downloading data from oracle.
I googled a lot and fortunately I found a website where it use dbc.Progress() and how to update the progress bar by connecting to tqdm with a file. But I still not sure how to do it for my own example. I tried and it doesn't work, could anyone help me with that? Thank you so much for your help.
https://towardsdatascience.com/long-callbacks-in-dash-web-apps-72fd8de25937
here is my code
I defined one tab, I include progress bar using dbc.Progress() and graph
progress_bar_heatmap=dbc.Progress(value=25, striped=True, animated=True,
children=['25%'],color='success',
style={'height':'20px'},
id="progress_bar_heatmap")
loading_timer_progress = dcc.Interval(id='loading_timer_progress',
interval=1000)
heatmap_graph = dcc.Graph(id="heatmap-graph", **graph_kwargs)
#wrap contour in dcc.loading's chilren so we can see loading signal
heatmap_loading=dcc.Loading(
id='loading-heatmap',
type='default',
children=heatmap_graph # wrap contour in loading's children
)
dcc.Tab(
[progress_bar_heatmap,loading_timer_progress, heatmap_loading],
label=label,
value='heatmap',
id='heatmap-tab',
className="single-tab",
selected_className="single-tab--selected",
)
in callback, I copied some codes from the above website,
#app.callback(
[
Output("heatmap-graph", "figure"),
Output("progress_bar_dts_heatmap", "value"),
],
[
Input("plot-dts", "n_clicks"),
Input('loading_timer_progress', 'n_intervals'),
],
prevent_initial_call=True, # disable output in the first load
)
def change_plot(n_clicks,n_intervals):
progress_bar_value=0
import sys
try:
with open('progress.txt', 'r') as file:
str_raw = file.read()
last_line = list(filter(None, str_raw.split('\n')))[-1]
percent = float(last_line.split('%')[0])
except: # no progress file created meansing it is creating
percent = 0
std_err_backup = sys.stderr
file_prog = open('progress.txt', 'w')
sys.stderr = file_prog
df=time_consuming_function()
result_str = f'Long callback triggered by {btn_name}. Result: {x:.2f}'
file_prog.close()
sys.stderr = std_err_backup
finally: # must do under all circustances
text = f'{percent:.0f}%'
fig=create_fig(df)
inside the time_consuming function
def time_consuming_function():
download_data_from_oracle()
# after that, I added below as website did
for i in tqdm(range(20)):
time.sleep(0.5)
return df
it doesn't work above, not sure which one is wrong?
I'm trying to define a function that will create lists from multiple text files and print a random element from one of the weighted lists. I've managed to get the function to work with random.choice for a single list.
enter code here
def test_rollitems():
my_commons = open('common.txt')
all_common_lines = my_commons.readlines()
common = []
for i in all_common_lines:
common.append(i)
y = random.choice(common)
print(y)
When I tried adding a second list to the function it wouldn't work and my program just closes when the function is called.
enter code here
def Improved_rollitem():
#create the lists from the files#
my_commons = open('common.txt')
all_common_lines= my_commons.readlines()
common = []
for i in all_common_lines:
common.append(i)
my_uncommons = open('uncommon.txt')
all_uncommon_lines =my_uncommons.readlines()
uncommon =[]
for i in all_uncommon_lines:
uncommon.apend(i)
y = random.choices([common,uncommon], [80,20])
print(y)
Can anyone offer any insight into what I'm doing wrong or missing ?
Nevermind. I figured this out on my own! Was having issues with Geany so I installed Pycharm and was able to work through the issue. Correct code is:
enter code here
def Improved_rollitem():
#create the lists from the files#
my_commons = open('common.txt')
all_common_lines= my_commons.readlines()
common = []
for i in all_common_lines:
common.append(i)
my_uncommons = open('uncommon.txt')
all_uncommon_lines =my_uncommons.readlines()
uncommon =[]
for i in all_uncommon_lines:
uncommon.append(i)
y = random.choices([common,uncommon], [.8,.20])
if y == [common]:
for i in [common]:
print(random.choice(i))
if y == [uncommon]:
for i in [uncommon]:
print(random.choice(i))
If there's a better way to do something like this, it would certainly be cool to know though.
I am running a modified version of the pyTorch deep Q tutorial which I have modified to pass in my own data rather than gym, and one additional input (two inputs in total)
Currently I am generating an individual state for each 'column' of inputs (not sure if this is the correct way though), when trying to pass the second input into my experience replay function it is returning:
__new__() takes 5 positional arguments but 7 were given
Code for expReplay():
class ReplayMemory(object):
def __init__(self, capacity):
self.capacity = capacity
self.memory = []
self.position = 0
def push(self, *args):
"""Saves a transition."""
if len(self.memory) < self.capacity:
self.memory.append(None)
self.memory[self.position] = Transition(*args)
self.position = (self.position + 1) % self.capacity
def sample(self, batch_size):
return random.sample(self.memory, batch_size)
def __len__(self):
return len(self.memory)
And my Triggering of the function:
memory.push(state,rsistate, action, next_state, next_rsi_state, reward)
If anyone has any examples of experience replay using multiple inputs please fire away! <3
My mistake, this was solved by modifying the Transition named tuple to include the additional inputs. Any information on multi input expreplay is still welcome.
How can I left align, rather than right align, the label in an iPython 3 widget, such as IntSlider? My ultimate goal is to left align a set of labeled widgets. This entails left aligning their labels, since the label is the leftmost element of each widget.
I've read the discussion in
Aligning TextBox Widgets in IPython Notebooks, but (a) it focuses on making more space for a right-aligned label, and (b) the proposed solution does not seem to affect the label width. (As an aside, I would be interested in finding cell-executable code that can reset the minimum label width.)
I've also read the discussion in Change the size of the label in an IPython notebook widget, but it doesn't seem to offer a simple solution.
Thanks for your help.
Addendum (2015-06-02):
Looks like widget.interactive() does not play nicely with the solution suggested by Jakob. Example:
from IPython.html import widgets
from IPython.display import display
def mySlider(text='', twidth=100, min=0, max=10, value=5):
c1 = widgets.HBox()
ints = widgets.IntSlider(min=min, max=max, value=value)
text = widgets.HTML(text, width=twidth)
c1.children = (text, ints)
return c1
s1 = mySlider('Test')
s2 = mySlider('TestTest')
s3 = mySlider('TestTestTest')
def process(a, b, c):
print([a, b, c])
widgets.interactive(
process,
a=s1.children[1].value,
b=s2.children[1].value,
c=s3.children[1].value
)
yields slider labels a, b, c with the usual alignment.
You can simply combine an IntSlider with an Html widget to create your custom widget like
from IPython.html import widgets
from IPython.display import display
def mySlider(text='', twidth=100):
c1 = widgets.HBox()
ints = widgets.IntSlider()
text = widgets.HTML(text, width=twidth)
c1.children = (text, ints)
return c1
Using this method, some widgets could look like
s1 = mySlider('Test')
s2 = mySlider('TestTest')
s3 = mySlider('TestTestTest')
display(s1,s2,s3)
Update to work with interact
To use these custom widgets with interact it is necessary to add some properties and callbacks. The interact method requires the widget.description and the widget.value arguments to setup the interactive widgets. As our container widget does not have these arguments, they are added manually. Moreover it is necessary to link the container.value with the IntSlider.value. This is once realized by a simple assignment, and more important via the on_trait_change method.
Finally, the interact methods calls the process function on widget.on_trait_change callbacks, thus the container.on_trait_change method is replaced by the IntSlider.on_trait_change call.
The updated code looks like:
def mySlider2(text='', twidth=100, min=0, max=10, value=5):
c1 = widgets.HBox()
ints = widgets.IntSlider(min=min, max=max, value=value)
text = widgets.HTML(text, width=twidth)
c1.children = (text, ints)
c1.description = text
c1.value = ints.value
def update(name, value):
c1.value = value
ints.on_trait_change(update,'value')
c1.on_trait_change = ints.on_trait_change
return c1
s1a = mySlider2('Test')
s2a = mySlider2('TestTest')
s3a = mySlider2('TestTestTest')
widgets.interactive(
process,
a=s1a,
b=s2a,
c=s3a
)