Say I have multiple lists:
names1 = [name11, name12, etc]
names2 = [name21, name22, etc]
names3 = [name31, name32, etc]
How do I create a for loop that combines the components of the lists in order ('name11name21name31', 'name11name21name32' and so on)?
I want to use this to name columns as I add them to a data frame. I tried like this:
Results['{}' .format(model_names[j]) + '{}' .format(Data_names[i])] = proba.tolist()
I am trying to take some results that I obtain as an array and introduce them one by one in a data frame and giving the columns names as I go on. It is for a machine learning model I am trying to make.
This is the whole code, I am sure it is messy because I am a beginner.
Train = [X_train_F, X_train_M, X_train_R, X_train_SM]
Test = [X_test_F, X_test_M, X_test_R, X_test_SM]
models_to_run = [knn, svc, forest, dtc]
model_names = ['knn', 'svc' ,'forest', 'dtc']
Data_names = ['F', 'M', 'R', 'SM']
Results = pd.DataFrame()
for T, t in zip(Train, Test):
for j, model in enumerate(models_to_run):
model.fit(T, y_train.values.ravel())
proba = model.predict_proba(t)
proba = pd.DataFrame(proba.max(axis=1))
proba = proba.to_numpy()
proba = proba.flatten()
Results['{}' .format(model_names[j]) + '{}' .format(Data_names[i])] = proba.tolist()
I dont know how to integrate 'i' in the loop, to use it to go through the list Data_names to add it to the column name. I am sure there is a cleaner way to do this. Please be gentle.
Edit: It currently gives me a data frame with 4 columns instead of 16 as it should, and it just adds the whole Data_names list to the column name.
How about:
Results= {}
for T, t, dname in zip(Train, Test, Data_names):
for mname, model in zip(model_names, models_to_run):
...
Results[(dname, mname)] = proba.to_list()
Results = pd.DataFrame(Results.values(), index=Results.keys()).T
Related
I have two dataframes: one comprising a large data set, allprice_df, with time price series for all stocks; and the other, init_df, comprising selective stocks and trade entry dates. I am trying to find the highest price for each ticker symbol and its associated date.
The following code works but it is time consuming, and I am wondering if there is a better, more Pythonic way to accomplish this.
# Initial call
init_df = init_df.assign(HighestHigh = lambda x:
highestHigh(x['DateIdentified'], x['Ticker'], allprice_df))
# HighestHigh function in lambda call
def highestHigh(date1,ticker,allp_df):
if date1.size == ticker.size:
temp_df = pd.DataFrame(columns = ['DateIdentified','Ticker'])
temp_df['DateIdentified'] = date1
temp_df['Ticker'] = ticker
else:
print("dates and tickers size mismatching")
sys.exit(1)
counter = itertools.count(0)
high_list = [getHigh(x,y,allp_df, next(counter)) for x, y in zip(temp_df['DateIdentified'],temp_df['Ticker'])]
return high_list
# Getting high for each ticker
def getHigh(dateidentified,ticker,allp_df, count):
print("trade %s" % count)
currDate = datetime.datetime.now().date()
allpm_df = allp_df.loc[((allp_df['Ticker']==ticker)&(allp_df['date']>dateidentified)&(allp_df['date']<=currDate)),['high','date']]
hh = allpm_df.iloc[:,0].max()
hd = allpm_df.loc[(allpm_df['high']==hh),'date']
hh = round(hh,2)
h_list = [hh,hd]
return h_list
# Split the list in to 2 columns one with price and the other with the corresponding date
init_df = split_columns(init_df,"HighestHigh")
# The function to split the list elements in to different columns
def split_columns(orig_df,col):
split_df = pd.DataFrame(orig_df[col].tolist(),columns=[col+"Mod", col+"Date"])
split_df[col+"Date"] = split_df[col+"Date"].apply(lambda x: x.squeeze())
orig_df = pd.concat([orig_df,split_df], axis=1)
orig_df = orig_df.drop(col,axis=1)
orig_df = orig_df.rename(columns={col+"Mod": col})
return orig_df
There are a couple of obvious solutions that would help reduce your runtime.
First, in your getHigh function, instead of using loc to get the date associated with the maximum value for high, use idxmax to get the index of the row associated with the high and then access that row:
hh, hd = allpm_df[allpm_df['high'].idxmax()]
This will replace two O(N) operations (finding the maximum in a list, and doing a list lookup using a comparison) with one O(N) operation and one O(1) operation.
Edit
In light of your information on the size of your dataframes, my best guess is that this line is probably where most of your time is being consumed:
allpm_df = allp_df.loc[((allp_df['Ticker']==ticker)&(allp_df['date']>dateidentified)&(allp_df['date']<=currDate)),['high','date']]
In order to make this faster, I would setup your data frame to include a multi-index when you first create the data frame:
index = pd.MultiIndex.from_arrays(arrays = [ticker_symbols, dates], names = ['Symbol', 'Date'])
allp_df = pd.Dataframe(data, index = index)
allp_df.index.sortlevel(level = 0, sort_remaining = True)
This should create a dataframe with a sorted, multi-level index associated with your ticker symbol and date. Doing this will reduce your search time tremendously. Once you do that, you should be able to access all the data associated with a ticker symbol and a given date-range by doing this:
allp_df[ticker, (dateidentified: currDate)]
which should return your data much more quickly. For more information on multi-indexing, check out this helpful Pandas tutorial.
I am trying to forecast sales for multiple time series I took from kaggle's Store item demand forecasting challenge. It consists of a long format time series for 10 stores and 50 items resulting in 500 time series stacked on top of each other. And for each store and each item, I have 5 years of daily records with weekly and annual seasonalities.
In total there are : 365.2days * 5years * 10stores *50items = 913000 records.
From my understanding based on what I've read so far on Hierarchical and Grouped time series, the whole dataframe could be structured as a Grouped Time Series and not simply as a strict Hierarchical Time Series as aggregation could be done at the store or item levels interchangeably.
I want to find a way to forecast all 500 time series (for store1_item1, store1_item2,..., store10_item50) for the next year (from 01-jan-2015 to 31-dec-2015) using the scikit-hts library and its AutoArimaModel function which is a wrapper function of pmdarima's AutoArima function.
To handle the two levels of seasonality, I added Fourier terms as exogenous features to deal with the annual seasonality while auto_arima deals with the weekly seasonality.
My problem is that I got an error at during prediction step.
Here's the error message :
ValueError: Provided exogenous values are not of the appropriate shape. Required (365, 4), got (365, 8).
I assume something is wrong with the exogenous dictionary but I do not know how to solve the issue as I'm using scikit-hts for the first time. To do this, I followed the official documentation of scikit-hts here.
EDIT :______________________________________________________________
I have not seen that a similar bug was reported on Github. Following the proposed fix that I implemented locally, I could have some results. However, even though there is no error when running the code, some of the forecasts are negative as raised in the comments below this post. And we even get disproportionate values for the positive ones.
Here are the plots for all the combinations of store and item. You can see that this seems to work for only one combination.
df.loc['2014','store_1_item_1'].plot()
predictions.loc['2015','store_1_item_1'].plot()
df.loc['2014','store_1_item_2'].plot()
predictions.loc['2015','store_1_item_2'].plot()
df.loc['2014','store_2_item_1'].plot()
predictions.loc['2015','store_2_item_1'].plot()
df.loc['2014','store_2_item_2'].plot()
predictions.loc['2015','store_2_item_2'].plot()
_____________________________________________________________________
Complete code:
# imports
import pandas as pd
from pmdarima.preprocessing import FourierFeaturizer
import hts
from hts.hierarchy import HierarchyTree
from hts.model import AutoArimaModel
from hts import HTSRegressor
# read data from the csv file
data = pd.read_csv('train.csv', index_col='date', parse_dates=True)
# Train/Test split with reduced size
train_data = data.query('store == [1,2] and item == [1, 2]').loc['2013':'2014']
test_data = data.query('store == [1,2] and item == [1, 2]').loc['2015']
# Create the stores time series
# For each timestamp group by store and apply sum
stores_ts = train_data.drop(columns=['item']).groupby(['date','store']).sum()
stores_ts = stores_ts.unstack('store')
stores_ts.columns = stores_ts.columns.droplevel(0)
stores_ts.columns = ['store_' + str(i) for i in stores_ts.columns]
# Create the items time series
# For each timestamp group by item and apply sum
items_ts = train_data.drop(columns=['store']).groupby(['date','item']).sum()
items_ts = items_ts.unstack('item')
items_ts.columns = items_ts.columns.droplevel(0)
items_ts.columns = ['item_' + str(i) for i in items_ts.columns]
# Create the stores_items time series
# For each timestamp group by store AND by item and apply sum
store_item_ts = train_data.pivot_table(index= 'date', columns=['store', 'item'], aggfunc='sum')
store_item_ts.columns = store_item_ts.columns.droplevel(0)
# Rename the columns as store_i_item_j
col_names = []
for i in store_item_ts.columns:
col_name = 'store_' + str(i[0]) + '_item_' + str(i[1])
col_names.append(col_name)
store_item_ts.columns = store_item_ts.columns.droplevel(0)
store_item_ts.columns = col_names
# Create a new dataframe and add the root level of the hierarchy as the sum of all stores (or all items)
df = pd.DataFrame()
df['total'] = stores_ts.sum(1)
# Concatenate all created dataframes into one df
# df is the dataframe that will be used for model training
df = pd.concat([df, stores_ts, items_ts, store_item_ts], 1)
# Build fourier terms for train and test sets
four_terms = FourierFeaturizer(365.2, 1)
# Build the exogenous features dataframe for training data
exog_train_df = pd.DataFrame()
for i in range(1, 3):
for j in range(1, 3):
_, exog = four_terms.fit_transform(train_data.query(f'store == {i} and item == {j}').sales)
exog.columns= [f'store_{i}_item_{j}_'+ x for x in exog.columns]
exog_train_df = pd.concat([exog_train_df, exog], axis=1)
exog_train_df['date'] = df.index
exog_train_df.set_index('date', inplace=True)
# add the exogenous features dataframe to df before training
df = pd.concat([df, exog_train_df], axis= 1)
# Build the exogenous features dataframe for test set
# It will be used only when using model.predict()
exog_test_df = pd.DataFrame()
for i in range(1, 3):
for j in range(1, 3):
_, exog_test = four_terms.fit_transform(test_data.query(f'store == {i} and item == {j}').sales)
exog_test.columns= [f'store_{i}_item_{j}_'+ x for x in exog_test.columns]
exog_test_df = pd.concat([exog_test_df, exog_test], axis=1)
# Build the hierarchy of the Grouped Time Series
stores = [i for i in stores_ts.columns]
items = [i for i in items_ts.columns]
store_items = col_names
# Exogenous features mapping
exog_store_items = {e: [v for v in exog_train_df.columns if v.startswith(e)] for e in store_items}
exog_stores = {e:[v for v in exog_train_df.columns if v.startswith(e)] for e in stores}
exog_items = {e:[v for v in exog_train_df.columns if v.find(e) != -1] for e in items}
exog_total = {'total':[v for v in exog_train_df.columns if v.find('FOURIER') != -1]}
# Merge all dictionaries
exog_to_merge = [exog_store_items, exog_stores, exog_items, exog_total]
exogenous = {k:v for x in exog_to_merge for k,v in x.items()}
# Build hierarchy
total = {'total': stores + items}
store_h = {k: [v for v in store_items if v.startswith(k)] for k in stores}
hierarchy = {**total, **store_h}
# Hierarchy tree automatically created by hts
ht = HierarchyTree.from_nodes(nodes=hierarchy, df=df, exogenous=exogenous)
# Instanciate the auto arima model using HTSRegressor
autoarima = HTSRegressor(model='auto_arima', D=1, m=7, seasonal=True, revision_method='OLS', n_jobs=12)
# Fit the model to the training df that includes time series and exog_train_df
# Set exogenous param to the previously built dictionary
model = autoarima.fit(df, hierarchy, exogenous=exogenous)
# Make predictions
# Set the exogenous_df param
predictions = model.predict(exogenous_df=exog_test_df, steps_ahead=365)
Other approaches I thought of and that I already implemented successfully for one series (for store 1 and item 1 for example) :
TBATS applied to each series independently inside a loop across all 500 time series
auto_arima (SARIMAX) with exogenous features (=Fourier terms to deal with the weekly and annual seasonalities) for each series independently + a loop across all 500 time series
What do you think of these approaches? Do you have other suggestions on how to scale ARIMA to multiple time series?
I also want to try LSTM but I'm new to data science and deep learning and do not know how to prepare the data. Should I keep the data in their original form (long format) and apply one hot encoding to train_data['store'] and train_data['item'] columns or should I start with the df I ended up with here?
I Hope this helped you in fixing the issue with exogenous regressors. To handle negative forecasts I would suggest you to try square root transformation.
Using sample data:
Product = [Galaxy_8, Galaxy_Note_9, Galaxy_Note_10, Galaxy_11]
I would like to create 4 data frames, each of the data frames contains respective sales information.
The problem is I would like to use index method to create data frames, for instance,
Expected output is:
Galaxy_8 = pd.DataFrame()
Galaxy_Note_9 = Pd.DataFrame()
Galaxy_Note_10 = pd.DataFrame()
Galaxy_11 = pd.DataFrame()
Imagine if the product list counts beyond 200, what is the most efficient way to achieve the desired outcome?
Thank you
If the sample list is like,
Product = ['Galaxy_8', 'Galaxy_Note_9', 'Galaxy_Note_10','Galaxy_11']
Then you can try like:
for var in Product:
globals()[var] = pd.DataFrame()
I have the following code:
for asset in port_close.columns.drop('P&L'):
forecasts = {}
am = arch_model(port_close[asset])
for i in range(20):
res = am.fit(first_obs=i, last_obs=i + end_loc, disp='off')
temp = res.forecast(horizon=1).variance
fcast = temp.iloc[i + end_loc - 1]
forecasts[fcast.name] = fcast
asset+'_vol' = pd.DataFrame(forecasts).T
I understand why that last line doesn't work, but in this for loop I want each name "asset" to be assigned to a separate dataframe. The "asset" in the first for loop references the columns ['AAPL', 'GOOGL', 'IBM']. So I want a dataframe called AAPL_vol and so on. Anyone know how to achieve this?
It sounds like you are trying to dynamically create a variable name, which isn't straightforward and there isn't really good reason for doing so. I suggest alternatively creating a dictionary of vols and assigning something like AAPL_vol as the key and the corresponding dataframe as the value.
Something like this.
vols = {}
for asset in port_close.columns.drop('P&L'):
forecasts = {}
am = arch_model(port_close[asset])
for i in range(20):
res = am.fit(first_obs=i, last_obs=i + end_loc, disp='off')
temp = res.forecast(horizon=1).variance
fcast = temp.iloc[i + end_loc - 1]
forecasts[fcast.name] = fcast
vols[asset+'_vol'] = pd.DataFrame(forecasts).T
I'm trying to find out averages and standard deviation of multiple columns of my dataset and then save them as a new column in a new dataframe. i.e. for every 'GROUP' in the dataset, I want one columns in the new dataframe with its average and SD. I came up with the following script but I'm not able to name it dynamically.
Average_F1_S_list, Average_F1_M_list, SD_F1_S_list, SD_F1_M_list = ([] for i in range(4))
Groups= DF['GROUP'].unique().tolist()
for key in Groups:
Average_F1_S = DF_DICT[key]['F1_S'].mean()
Average_F1_S_list.append(Average_F1_S)
SD_F1_S = DF_DICT[key]['F1_S'].std()
SD_F1_S_list.append(SD_F1_S)
Average_F1_M = DF_DICT[key]['F1_M'].mean()
Average_F1_M_list.append(Average_F1_M)
SD_F1_M = DF_DICT[key]['F1_M'].std()
SD_F1_M_list.append(SD_F1_M)
df=pd.DataFrame({'Group':Groups,
'Average_F1_S':Average_F1_S_list,'Standard_Dev_F1_S':SD_F1_S_list,
'Average_F1_M':Average_F1_M_list,'Standard_Dev_F1_M':SD_F1_M_list},
columns=['Group','Average_F1_S','Standard_Dev_F1_S','Average_F1_M', 'Standard_Dev_F1_M'])
This will not be a good solution as there are too many features. Is there any way I can create the lists dynamically?
This should do the trick! Hope this helps
# These are all the keys you want
key_names = ['F1_S', 'F1_M']
# Holds the data you want to pass to the dataframe.
df_info = {'Groups': Groups}
for group_name in Groups:
# For each group in the groups, we iterate over all the keys we want.
for key in key_names:
# Generate a keyname that you want for your dataframe.
avg_key_name = key + '_Average'
std_key_name = key + '_Standard_Dev'
if avg_key_name not in df_info:
df_info[avg_key_name] = []
df_info[std_key_name] = []
df_info[avg_key_name].append(DF_DICT[group_name][key].mean())
df_info[std_key_name].append(DF_DICT[group_name][key].std())
df = pd.DataFrame(df_info)