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az.plot_posterior(
    trace,
    var_names=['intercept', 'coefficient_'],
    filter_vars='like'
);

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trace_vars = [i.replace('coefficient_','') for i in dir(trace['posterior']) if i.startswith("coefficient_") or i=='intercept']
trace_coeff_vars = [i for i in dir(trace['posterior']) if i.startswith("coefficient_") or i=='intercept']
trace_alpha_vars = [i for i in dir(trace['posterior']) if i.startswith("alpha_")]
trace_gamma_vars = [i for i in dir(trace['posterior']) if i.startswith("gamma_")]

model_results = pd.DataFrame({
    "variable": trace_vars,
    "coefficient": [float(trace['posterior'][i].mean()) for i in trace_coeff_vars]
}).merge(
    pd.DataFrame({

abhijeet-talaulikar

def scale(x, y):
  return x * y.sum() / x.sum()

with pm.Model() as mmm:

    target = media_transformed['REVENUE'] / media_transformed['REVENUE'].mean()

    media_contributions = []

    for channel in channel_priors.keys():

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# define coefficient
channel_prior = channel_priors[channel]
channel_coefficient = pm.TruncatedNormal(f"coefficient_{channel}", mu=channel_prior, sigma=0.0001, lower=0, upper=0.15)

# define saturation
alpha = pm.Uniform(f"alpha_{channel}", lower=0.5, upper=2)
gamma = pm.Uniform(f"gamma_{channel}", lower=0.5, upper=1.5)

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channel_priors = {
    "CTV": 0.02,
    "DISPLAY": 0.05,
    "DIRECT_MAIL": 0.02,
    "EMAIL": 0.03,
    "PAID_SOCIAL": 0.08,
    "PAID_SEARCH": 0.14,
    "TV": 0.01
}

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media = pd.read_csv('media.csv', parse_dates=['DATE'])
media = media.resample('W-Mon', label='left', closed='left', on='DATE').sum()

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def hill_transform(x, alpha, gamma):
    return 1 / (1 + (x/gamma)**-alpha)

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lag = {
    "CTV": 2,
    "DISPLAY": 2,
    "DIRECT_MAIL": 2,
    "EMAIL": 2,
    "PAID_SOCIAL": 2,
    "PAID_SEARCH": 1,
    "TV": 3
}

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def fp_growth(transaction_db, min_sup, fp_list, prefix = []):
    
    ####### STEP A: CONCATENATE #######
    # Get one itemsets which meet minimum support
    one_itemset_dict = get_one_itemsets(transaction_db, min_sup)
    
    # Concanenate frequent patterns with prefix
    freq_patterns = []
    for key,val in one_itemset_dict.items():
        pattern = prefix + [key]

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def insert_tree(prefix, suffix, current_root, fp_tree, node_link_dict, transaction_count):
    
    # Check if route to the prefix node exists in tree
    # If exists, get the node
    new_root = tree_contains_node(prefix, current_root, fp_tree)
    
    if new_root == False:
        # If route doesn't exist, create new child node to current root
        new_root = {'name':prefix, 'count':transaction_count, 'parent': current_root}