Related
I have a very large spatial dataset stored in a dataframe. I am taking a slice of that dataframe into a new smaller subset to run further calculations.
The data has x, y and z coordinates with a number of additional columns, some of which are text and some are numeric. The x and y coordinates are on a defined grid and have a known separation.
Data looks like this
x,y,z,text1,text2,text3,float1,float2
75000,45000,120,aa,bbb,ii,12,0.2
75000,45000,110,bb,bbb,jj,22,0.9
75000,45100,120,aa,bbb,ii,11,1.8
75000,45100,110,bb,bbb,jj,45,2.4
75000,45100,100,bb,ccc,ii,13.6,1
75100,45000,120,bb,ddd,jj,8.2,2.1
75100,45000,110,bb,ddd,ii,12,0.6
For each x and y pair I want to iterate over a two series of text values and do three things in the z direction.
Calculate the average of one numeric value for all the values with a third specific text value
Sum another numeric value for all the values with the same text value
Write the a resultant table of 'x, y, average, sum' to a csv.
My code does part three (albeit very slowly) but doesn't calculate 1 or 2 or at least I don't appear to get the average and sum calculations in my output.
What have I done wrong and how can I speed it up?
for text1 in text_list1:
for text2 in text_list2:
# Get the data into smaller dataframe
df = data.loc[ (data["textfield1"] == text1) & (data["textfield2"] == text2 ) ]
#Get the minimum and maximum x and y
minXw = df['x'].min()
maxXw = df['x'].max()
minYw = df['y'].min()
maxYw = df['y'].max()
# dictionary for quicker printing
dict_out = {}
rows_list = []
# Make output filename
filenameOut = text1+"_"+text2+"_Values.csv"
# Start looping through x values
for x in np.arange(minXw, maxXw, x_inc):
xcount += 1
# Start looping through y values
for y in np.arange(minYw, maxYw, y_inc):
ycount += 1
# calculate average and sum
ave_val = df.loc[df['textfield3'] == 'text3', 'float1'].mean()
sum_val = df.loc[df['textfield3'] == 'text3', 'float2'].sum()
# Make Dictionary of output values
dict_out = dict([('text1', text1),
('text2', text2),
('text3', df['text3']),
('x' , x-x_inc),
('y' , y-y_inc),
('ave' , ave_val),
('sum' , sum_val)])
rows_list_c.append(dict_out)
# Write csv
columns = ['text1','text2','text3','x','y','ave','sum']
with open(filenameOut, 'w') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=columns)
writer.writeheader()
for data in dict_out:
writer.writerow(data)
My resultant csv gives me:
text1,text2,text3,x,y,ave,sum
text1,text2,,74737.5,43887.5,nan,0.0
text1,text2,,74737.5,43912.5,nan,0.0
text1,text2,,74737.5,43937.5,nan,0.0
text1,text2,,74737.5,43962.5,nan,0.0
Not really clear what you're trying to do. But here is a starting point
If you only need to process rows with a specific text3value, start by filtering out the other rows:
df = df[df.text3=="my_value"]
If at this point, you do not need text3 anymore, you can also drop it
df = df.drop(columns="text3")
Then you process several sub dataframes, and write each of them to their own csv file. groupby is the perfect tool for that:
for (text1, text2), sub_df in df.groupby(["text1", "text2"]):
filenameOut = text1+"_"+text2+"_Values.csv"
# Process sub df
output_df = process(sub_df)
# Write sub df
output_df.to_csv(filenameOut)
Note that if you keep your data as a DataFrame instead of converting it to a dict, you can use the DataFrame to_csv method to simply write the output csv.
Now let's have a look at the process function (Note that you dont really need to make it a separate function, you could as well dump the function body in the for loop).
At this point, if I understand correctly, you want to compute the sum and the average of every rows that have the same x and y coordinates. Here again you can use groupby and the agg function to compute the mean and the sum of the group.
def process(sub_df):
# drop the text1 and text2 columns since they are in the filename anyway
out = sub_df.drop(columns=["text1","text2"])
# Compute mean and max
return out.groupby(["x", "y"]).agg(ave=("float1", "mean"), sum=("float2", "sum"))
And that's preety much it.
Bonus: 2-liner version (but don't do that...)
for (text1, text2), sub_df in df[df.text3=="my_value"].drop(columns="text3").groupby(["text1", "text2"]):
sub_df.drop(columns=["text1","text2"]).groupby(["x", "y"]).agg(ave=("float1", "mean"), sum=("float2", "sum")).to_csv(text1+"_"+text2+"_Values.csv")
To do this in an efficient way in pandas you will need to use groupby, agg and the in-built to_csv method rather than using for loops to construct lists of data and writing each one with the csv module. Something like this:
groups = data[data["text1"].isin(text_list1) & data["text2"].isin(text_list2)] \
.groupby(["text1", "text2"])
for (text1, text2), group in groups:
group.groupby("text3") \
.agg({"float1": np.mean, "float2": sum}) \
.to_csv(f"{text1}_{text2}_Values.csv")
It's not clear exactly what you're trying to do with the incrementing of x and y values, which is also what makes your current code very slow. To present sums and averages of the floating point columns by intervals of x and y, you could make bin columns and group by those too.
data["x_bin"] = (data["x"] - data["x"].min()) // x_inc
data["y_bin"] = (data["y"] - data["y"].min()) // y_inc
groups = data[data["text1"].isin(text_list1) & data["text2"].isin(text_list2)] \
.groupby(["text1", "text2"])
for (text1, text2), group in groups:
group.groupby(["text3", "x_bin", "y_bin"]) \
.agg({"x": "first", "y": "first", "float1": np.mean, "float2": sum}) \
.to_csv(f"{text1}_{text2}_Values.csv")
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.
I am trying to produce a box plot using matplotlib with data from nested dictionaries. Below is a rough outline of the structure of dictionary in question.
m_data = {scenario:{variable:{'model_name':value, ''model_name':value ...}
One issue is that I want to look at the change in the models output between the two different scenarios ( scenario 1 [VAR1] - scenario 2 [VAR2]) and then plot this difference in a box plot.
I have managed to do this, however, I want to be able to label the outliers with the model name. My current method separates the keys from the values, therefore the outlier data point has no name associated with it anymore.
#BOXPLOT
#set up blank lists
future_rain = []
past_rain = []
future_temp = []
past_temp = []
#single out the values for each model from the nested dictioaries
for key,val in m_data[FUTURE_SCENARIO][VAR1].items():
future_rain.append(val)
for key,val in m_data[FUTURE_SCENARIO][VAR2].items():
future_temp.append(val)
for key,val in m_data['historical'][VAR1].items():
past_rain.append(val)
for key,val in m_data['historical'][VAR2].items():
past_temp.append(val)
#blanks for final data
bx_plt_rain = []
bx_plt_temp = []
#allow for the subtration of two lists
zip_object = zip(future_temp, past_temp)
for future_temp_i, past_temp_i in zip_object:
bx_plt_temp.append(future_temp_i - past_temp_i)
zip_object = zip(future_rain, past_rain)
for future_rain_i, past_rain_i in zip_object:
bx_plt_rain.append(future_rain_i - past_rain_i)
#colour ouliers red
c = 'red'
outlier_col = {'flierprops': dict(color =c, markeredgecolor=c)}
#plot
bp = plt.boxplot(bx_plt_rain, patch_artist=True, showmeans=True, vert= False, meanline=True, **outlier_col)
bp['boxes'][0].set(facecolor = 'lightgrey')
plt.show()
If anyone knows of a workaround for this I would be extremely grateful.
As a bit of a hack you could create a function that looks through the dict for the outlier value and returns the key.
def outlier_name(outlier_val, inner_dict):
for key, value in inner_dict.items():
if value == outlier_val:
return key
This could be pretty intensive if your data sets are large.
I'm trying zero-shot classification. I get an output like below
[{'labels': ['rep_appreciation',
'cx_service_appreciation',
'issue_resolved',
'recommend_product',
'callback_realted',
'billing_payment_related',
'disppointed_product'],
'scores': [0.9198898673057556,
0.8672246932983398,
0.79215407371521,
0.6239275336265564,
0.4782547056674957,
0.39024001359939575,
0.010263209231197834],
'sequence': 'Alan Edwards provided me with nothing less the excellent assistance'}
Above is output for one row in a data frame
I'm hoping to finally build a data frame columns and output values mapped like below. 1s for labels if the scores are above certain threshold
Any nudge/help to solve this is highly appreciated.
Define a function which returns a key : value dictionary for every row, with key being the label and value being 1/0 based on threshold
def get_label_score_dict(row, threshold):
result_dict = dict()
for _label, _score in zip(row['labels'], row['scores']):
if _score > threshold:
result_dict.update({_label: 1})
else:
result_dict.update({_label: 0})
return result_dict
Now if you have a list_of_rows with each row being in the form as shown above, then you can use the map function to get the above mentioned dictionary for every row. Once you get this, convert it into a DataFrame.
th = 0.5 #whatever threshold value you want
result = list(map(lambda x: get_label_score_dict(x, th), list_of_rows))
result_df = pd.DataFrame(result)
I am using the scipy stats module to calculate the linear regression. ie
slope, intercept, r_value, p_value, std_err
= stats.linregress(data['cov_0.0075']['num'],data['cov_0.0075']['com'])
where data is a dictionary containing several 'cov_x' keys corresponding to a dataframe with columns 'num' and 'com'
I want to be able to loop through this dictionary and do linear regression on each 'cov_x'. I am not sure how to do this. I tried:
for i in data:
slope_+str(i), intercept+str(i), r_value+str(i),p_value+str(i),std_err+str(i)= stats.linregress(data[i]['num'],data[i]['com'])
Essentially I want len(x) slope_x values.
You could use a list comprehension to collect all the stats.linregress return values:
result = [stats.linregress(df['num'],df['com']) for key, df in data.items()]
result is a list of 5-tuples. To collect all the first, second, third, etc... elements from each 5-tuple into separate lists, use zip(*[...]):
slopes, intercepts, r_values, p_values, stderrs = zip(*result)
You should be able to do what you're trying to, but there are a couple of things you should watch out for.
First, you can't add a string to a variable name and store it that way. No plus signs on the left of the equals sign. Ever.
You should be able to accomplish what you're trying to do, however. Just make sure that you use the dict data type if you want string indexing.
import scipy.stats as stats
import pandas as pd
import numpy as np
data = {}
l = ['cov_0.0075','cov_0.005']
for i in l:
x = np.random.random(100)
y = np.random.random(100)+15
d = {'num':x,'com':y}
df = pd.DataFrame(data=d)
data[i] = df
slope = {}
intercept = {}
r_value = {}
p_value = {}
std_error = {}
for i in data:
slope[str(i)], \
intercept[str(i)], \
r_value[str(i)],\
p_value[str(i)], std_error[str(i)]= stats.linregress(data[i]['num'],data[i]['com'])
print(slope,intercept,r_value,p_value,std_error)
should work just fine. Otherwise, you can store individual values and put them in a list later.