Update code formatting with modified blacken-docs

_episodes/06-Data_Discussion.md

@@ -20,9 +20,9 @@ keypoints:
 Here we will import all the required libraries for the rest of the tutorial. All scikit-learn and PyTorch functions will be imported later on when they are required.
 
 ~~~
-import pandas as pd # to store data as dataframe
-import numpy as np # for numerical calculations such as histogramming
-import matplotlib.pyplot as plt # for plotting
+import pandas as pd  # to store data as dataframe
+import numpy as np  # for numerical calculations such as histogramming
+import matplotlib.pyplot as plt  # for plotting
 ~~~
 {: .language-python}
 
@@ -36,9 +36,10 @@ np.__version__
 Let's set the random seed that we'll be using. This reduces the randomness when you re-run the notebook
 
 ~~~
-seed_value = 420 # 42 is the answer to life, the universe and everything
-from numpy.random import seed # import the function to set the random seed in NumPy
-seed(seed_value) # set the seed value for random numbers in NumPy
+seed_value = 420  # 42 is the answer to life, the universe and everything
+from numpy.random import seed  # import the function to set the random seed in NumPy
+
+seed(seed_value)  # set the seed value for random numbers in NumPy
 ~~~
 {: .language-python}
 
@@ -51,7 +52,7 @@ The dataset we will use in this tutorial is simulated ATLAS data. Each event cor
 # In this notebook we only process the main signal ggH125_ZZ4lep and the main background llll,
 # for illustration purposes.
 # You can add other backgrounds after if you wish.
-samples = ['llll','ggH125_ZZ4lep']
+samples = ["llll", "ggH125_ZZ4lep"]
 ~~~
 {: .language-python}
 
@@ -62,26 +63,28 @@ Here we will format the dataset $$(x_i, y_i)$$ so we can explore! First, we need
 ~~~
 # get data from files
 
-DataFrames = {} # define empty dictionary to hold dataframes
-for s in samples: # loop over samples
-    DataFrames[s] = pd.read_csv('/kaggle/input/4lepton/'+s+".csv") # read .csv file
+DataFrames = {}  # define empty dictionary to hold dataframes
+for s in samples:  # loop over samples
+    DataFrames[s] = pd.read_csv("/kaggle/input/4lepton/" + s + ".csv")  # read .csv file
 
-DataFrames['ggH125_ZZ4lep'] # print signal data to take a look
+DataFrames["ggH125_ZZ4lep"]  # print signal data to take a look
 ~~~
 {: .language-python}
 
 Before diving into machine learning, think about whether there are any things you should do to clean up your data. In the case of this Higgs analysis, Higgs boson decays should produce 4 electrons or 4 muons or 2 electrons and 2 muons. Let's define a function to keep only events which produce 4 electrons or 4 muons or 2 electrons and 2 muons.
 
 ~~~
 # cut on lepton type
-def cut_lep_type(lep_type_0,lep_type_1,lep_type_2,lep_type_3):
-# first lepton is [0], 2nd lepton is [1] etc
-# for an electron lep_type is 11
-# for a muon lep_type is 13
-# only want to keep events where one of eeee, mumumumu, eemumu
+def cut_lep_type(lep_type_0, lep_type_1, lep_type_2, lep_type_3):
+    # first lepton is [0], 2nd lepton is [1] etc
+    # for an electron lep_type is 11
+    # for a muon lep_type is 13
+    # only want to keep events where one of eeee, mumumumu, eemumu
     sum_lep_type = lep_type_0 + lep_type_1 + lep_type_2 + lep_type_3
-    if sum_lep_type==44 or sum_lep_type==48 or sum_lep_type==52: return True
-    else: return False
+    if sum_lep_type == 44 or sum_lep_type == 48 or sum_lep_type == 52:
+        return True
+    else:
+        return False
 ~~~
 {: .language-python}
 
@@ -91,11 +94,15 @@ We then need to apply this function on our DataFrames.
 # apply cut on lepton type
 for s in samples:
     # cut on lepton type using the function cut_lep_type defined above
-    DataFrames[s] = DataFrames[s][ np.vectorize(cut_lep_type)(DataFrames[s].lep_type_0,
-                              		                      DataFrames[s].lep_type_1,
-                                          	              DataFrames[s].lep_type_2,
-                                                  	      DataFrames[s].lep_type_3) ]
-DataFrames['ggH125_ZZ4lep'] # print signal data to take a look
+    DataFrames[s] = DataFrames[s][
+        np.vectorize(cut_lep_type)(
+            DataFrames[s].lep_type_0,
+            DataFrames[s].lep_type_1,
+            DataFrames[s].lep_type_2,
+            DataFrames[s].lep_type_3,
+        )
+    ]
+DataFrames["ggH125_ZZ4lep"]  # print signal data to take a look
 ~~~
 {: .language-python}
 
@@ -131,12 +138,12 @@ DataFrames['ggH125_ZZ4lep'] # print signal data to take a look
 In any analysis searching for <span style="color:orange">signal</span> one wants to optimise the use of various input variables. Often, this optimisation will be to find the best <span style="color:orange">signal</span> to <span style="color:blue">background</span> ratio. Here we define histograms for the variables that we'll look to optimise.
 
 ~~~
-lep_pt_2 = { # dictionary containing plotting parameters for the lep_pt_2 histogram
+lep_pt_2 = {  # dictionary containing plotting parameters for the lep_pt_2 histogram
     # change plotting parameters
-    'bin_width':1, # width of each histogram bin
-    'num_bins':13, # number of histogram bins
-    'xrange_min':7, # minimum on x-axis
-    'xlabel':r'$lep\_pt$[2] [GeV]', # x-axis label
+    "bin_width": 1,  # width of each histogram bin
+    "num_bins": 13,  # number of histogram bins
+    "xrange_min": 7,  # minimum on x-axis
+    "xlabel": r"$lep\_pt$[2] [GeV]",  # x-axis label
 }
 ~~~
 {: .language-python}
@@ -162,7 +169,10 @@ lep_pt_2 = { # dictionary containing plotting parameters for the lep_pt_2 histog
 Now we define a dictionary for the histograms we want to plot.
 
 ~~~
-SoverB_hist_dict = {'lep_pt_2':lep_pt_2,'lep_pt_1':lep_pt_1} # add a histogram here if you want it plotted
+SoverB_hist_dict = {
+    "lep_pt_2": lep_pt_2,
+    "lep_pt_1": lep_pt_1,
+}  # add a histogram here if you want it plotted
 ~~~
 {: .language-python}
 
@@ -172,6 +182,7 @@ We're not doing any machine learning just yet! We're looking at the variables we
 
 ~~~
 from my_functions import plot_SoverB
+
 plot_SoverB(DataFrames, SoverB_hist_dict)
 ~~~
 {: .language-python}

_episodes/07-Data_Preprocessing.md

@@ -20,7 +20,7 @@ keypoints:
 It's almost time to build a machine learning model! First we choose the variables to use in our machine learning model.
 
 ~~~
-ML_inputs = ['lep_pt_1','lep_pt_2'] # list of features for ML model
+ML_inputs = ["lep_pt_1", "lep_pt_2"]  # list of features for ML model
 ~~~
 {: .language-python}
 
@@ -34,20 +34,32 @@ ML_inputs = ['lep_pt_1','lep_pt_2'] # list of features for ML model
 # containing all the data and one array of categories
 # of length n_samples
 
-all_MC = [] # define empty list that will contain all features for the MC
-for s in samples: # loop over the different samples
-    if s!='data': # only MC should pass this
-        all_MC.append(DataFrames[s][ML_inputs]) # append the MC dataframe to the list containing all MC features
-X = np.concatenate(all_MC) # concatenate the list of MC dataframes into a single 2D array of features, called X
-
-all_y = [] # define empty list that will contain labels whether an event in signal or background
-for s in samples: # loop over the different samples
-    if s!='data': # only MC should pass this
-        if 'H125' in s: # only signal MC should pass this
-            all_y.append(np.ones(DataFrames[s].shape[0])) # signal events are labelled with 1
-        else: # only background MC should pass this
-            all_y.append(np.zeros(DataFrames[s].shape[0])) # background events are labelled 0
-y = np.concatenate(all_y) # concatenate the list of labels into a single 1D array of labels, called y
+all_MC = []  # define empty list that will contain all features for the MC
+for s in samples:  # loop over the different samples
+    if s != "data":  # only MC should pass this
+        all_MC.append(
+            DataFrames[s][ML_inputs]
+        )  # append the MC dataframe to the list containing all MC features
+X = np.concatenate(
+    all_MC
+)  # concatenate the list of MC dataframes into a single 2D array of features, called X
+
+all_y = (
+    []
+)  # define empty list that will contain labels whether an event in signal or background
+for s in samples:  # loop over the different samples
+    if s != "data":  # only MC should pass this
+        if "H125" in s:  # only signal MC should pass this
+            all_y.append(
+                np.ones(DataFrames[s].shape[0])
+            )  # signal events are labelled with 1
+        else:  # only background MC should pass this
+            all_y.append(
+                np.zeros(DataFrames[s].shape[0])
+            )  # background events are labelled 0
+y = np.concatenate(
+    all_y
+)  # concatenate the list of labels into a single 1D array of labels, called y
 ~~~
 {: .language-python}
 
@@ -69,17 +81,18 @@ Now we separate our data into a training and test set.
 from sklearn.model_selection import train_test_split
 
 # make train and test sets
-X_train,X_test, y_train,y_test = train_test_split(X, y,
-                                                  test_size=0.33,
-                                                  random_state=seed_value ) # set the random seed for reproducibility
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.33, random_state=seed_value
+)  # set the random seed for reproducibility
 ~~~
 {: .language-python}
 
 Machine learning models may have difficulty converging before the maximum number of iterations allowed if the data aren't normalized. Note that you must apply the same scaling to the test set for meaningful results (we'll apply the scaling to the test set in the next step). There are a lot of different methods for normalization of data. We will use the built-in StandardScaler for standardization. The `StandardScaler` ensures that all numerical attributes are scaled to have a mean of 0 and a standard deviation of 1 before they are fed to the machine learning model. This type of preprocessing is common before feeding data into machine learning models and is especially important for neural networks.
 
 ~~~
 from sklearn.preprocessing import StandardScaler
-scaler = StandardScaler() # initialise StandardScaler
+
+scaler = StandardScaler()  # initialise StandardScaler
 
 # Fit only to the training data
 scaler.fit(X_train)