def updated_model_with_errors(parameter):
layout = {
'yaxis': {
'range': [0, 450],
'title': 'sales'
},
'xaxis': {
'title': 'ad spend'
}
}
inputs = list(range(1500, 4500, 250))
predictions = list(
map(lambda ad_spend: parameter * ad_spend, observed_ad_spends))
data_trace = trace_values([2000, 3500, 4000], [260, 445, 490],
name='actual sales')
predictions_trace = trace_values(observed_ad_spends,
predictions,
'lines',
name='predictions')
y_values_y_hats = list(zip(observed_sales, predictions))
errors = list(map(lambda pair: pair[0] - pair[1], y_values_y_hats))
error_traces = error_line_traces(observed_ad_spends, observed_sales,
errors)
return plot([data_trace, predictions_trace] + error_traces)
def plot_data_and_model():
inputs = list(range(1500, 4500, 250))
predictions = list(map(lambda input: .15*input,inputs))
predictions_trace = trace_values(inputs, predictions, 'lines', name = 'predictions')
data_trace = trace_values([2000, 3500, 4000], [260, 445, 490], name = 'actual sales')
layout = {'yaxis': {'range': [0, 18], 'title': 'sales'}, 'xaxis': {'title': 'ad spend'}}
return plot([data_trace, predictions_trace])
def updated_model_with_errors(parameter):
layout = {'yaxis': {'range': [0, 18], 'title': 'shot distance'}, 'xaxis': {'title': 'shot angle'}}
predictions = list(map(lambda angle: parameter*angle, observed_shot_angles))
actual_trace = trace_values(observed_shot_angles, observed_distances, name = 'actual shots')
predictions_trace = trace_values(observed_shot_angles, predictions, 'lines', name = 'predictions')
y_values_y_hats = list(zip(observed_distances, predictions))
errors = list(map(lambda pair: pair[0] - pair[1], y_values_y_hats))
error_traces = error_line_traces(observed_shot_angles, observed_distances, errors)
return py.plot([actual_trace, predictions_trace] + error_traces)
def delta_x_trace(list_of_terms, x_value, delta):
initial_f = output_at(list_of_terms, x_value)
trace = trace_values(x_values=[x_value, x_value + delta],
text=[str(x_value), str(x_value + delta)],
y_values=[initial_f, initial_f], mode = 'lines+text',
name = 'x2 - x1 = ' + str(initial_f + delta) + ' - ' + str(initial_f) + ' = ' + str(delta))
return trace
def delta_f_trace(list_of_terms, x_value, delta_x):
initial_f = output_at(list_of_terms, x_value)
delta_y = delta_f(list_of_terms, x_value, delta_x)
trace = trace_values(x_values=[x_value + delta_x, x_value + delta_x],
y_values=[initial_f, initial_f + delta_y],
text=[str(initial_f), str(initial_f + delta_y)], mode = 'lines+text', name = 'y2 - y1 = ' + str(initial_f + delta_y) + ' - ' + str(initial_f) + ' = ' + str(delta_y))
return trace
def plot_data_and_errors():
inputs = [.30, .40, .50, .60, .70]
predictions = list(map(lambda angle: 40*angle,inputs))
predictions_trace = trace_values(inputs, predictions, 'lines', name = 'predictions')
errors = [-4, -9, -11]
error_traces = error_line_traces(observed_shot_angles, observed_distances, errors)
return py.plot([data_trace, predictions_trace] + error_traces)
def delta_x_trace(list_of_terms, x_value, delta):
initial_f = output_at(list_of_terms, x_value)
trace = trace_values(x_values=[x_value, x_value + delta],
text=[str(x_value), str(x_value + delta)],
y_values=[initial_f, initial_f], mode = 'lines+text',
name = 'x2 - x1 = ' + str(initial_f + delta) + ' - ' + str(initial_f) + ' = ' + str(delta),
options = {'textposition': 'bottom'})
return trace
def delta_f_trace(list_of_terms, x_value, delta_x):
initial_f = output_at(list_of_terms, x_value)
delta_y = delta_f(list_of_terms, x_value, delta_x)
trace = trace_values(x_values=[x_value + delta_x, x_value + delta_x],
y_values=[initial_f, initial_f + delta_y],
text=[str(initial_f), str(initial_f + delta_y)], mode = 'lines+text', name = 'y2 - y1 = ' + str(initial_f + delta_y) + ' - ' + str(initial_f) + ' = ' + str(delta_y),
options = {'textposition': 'right'})
return trace
def plot_data_and_errors():
inputs = list(range(1500, 4500, 250))
predictions = list(map(lambda input: .15*input,inputs))
predictions_trace = trace_values(inputs, predictions, 'lines', name = 'predictions')
errors = [-40, -80, -110]
ad_spends = [2000, 3500, 4000]
sales = [260, 445, 490]
error_traces = error_line_traces(ad_spends, sales, errors)
return plot([data_trace, predictions_trace] + error_traces)
def delta_x_trace(function, x_value, delta):
initial_f = function(x_value)
trace = trace_values(x_values=[x_value, x_value + delta],
text=[str(x_value),
str(x_value + delta)],
y_values=[initial_f, initial_f],
mode='lines+text',
name='x2 - x1 = ' + str(initial_f + delta) + ' - ' +
str(initial_f) + ' = ' + str(delta),
options={'textposition': 'bottom left'})
return trace
def delta_f_trace(function, x_value, delta_x):
initial_f = function(x_value)
delta_y = delta_f(function, x_value, delta_x)
trace = trace_values(x_values=[x_value + delta_x, x_value + delta_x],
y_values=[initial_f, initial_f + delta_y],
text=[str(initial_f),
str(initial_f + delta_y)],
mode='lines+text',
name='y2 - y1 = ' + str(initial_f + delta_y) + ' - ' +
str(initial_f) + ' = ' + str(delta_y),
options={'textposition': 'top right'})
return trace
def plot_data_and_model():
model_trace = trace_values(angles,
predicted_distances,
mode='lines',
name='model')
layout = {
'yaxis': {
'range': [0, 18],
'title': 'shot distance'
},
'xaxis': {
'title': 'shot angle'
}
}
return plot([data_trace, model_trace])
from graph import trace_values, plot
from graph import m_b_trace, plot, m_b_data, trace_values
from error import error_line_traces
angles = [.1, .2, .3, .4, .5, .6, .7]
predicted_distances = list(map(lambda angle: 40 * angle, angles))
model_trace = trace_values(angles, predicted_distances, mode = 'lines', name = 'model')
data_trace = trace_values([2000, 3500, 4000], [260, 445, 490], name = 'actual sales')
observed_ad_spends = [2000, 3500, 4000]
observed_sales = [260, 445, 490]
def plot_data_and_model():
inputs = list(range(1500, 4500, 250))
predictions = list(map(lambda input: .15*input,inputs))
predictions_trace = trace_values(inputs, predictions, 'lines', name = 'predictions')
data_trace = trace_values([2000, 3500, 4000], [260, 445, 490], name = 'actual sales')
layout = {'yaxis': {'range': [0, 18], 'title': 'sales'}, 'xaxis': {'title': 'ad spend'}}
return plot([data_trace, predictions_trace])
def plot_data_and_errors():
inputs = list(range(1500, 4500, 250))
predictions = list(map(lambda input: .15*input,inputs))
predictions_trace = trace_values(inputs, predictions, 'lines', name = 'predictions')
errors = [-40, -80, -110]
ad_spends = [2000, 3500, 4000]
sales = [260, 445, 490]
error_traces = error_line_traces(ad_spends, sales, errors)
return plot([data_trace, predictions_trace] + error_traces)
def updated_model_with_errors(parameter):
layout = {'yaxis': {'range': [0, 450], 'title': 'sales'}, 'xaxis': {'title': 'ad spend'}}
def function_values_trace(list_of_terms, x_values):
function_values = list(map(lambda x: output_at(list_of_terms, x),
x_values))
return trace_values(x_values, function_values, mode='line')
def function_values_trace(list_of_terms, x_values):
function_values = list(map(lambda x: output_at(list_of_terms, x),x_values))
return trace_values(x_values, function_values, mode = 'line')
def derivative_values_trace(function, x_values, delta_x):
derivative_values = list(
map(lambda x: derivative_of(function, x, delta_x), x_values))
return trace_values(x_values, derivative_values, mode='lines')
def function_values_trace(function, x_values):
function_values = list(map(lambda x: function(x), x_values))
return trace_values(x_values, function_values, mode='lines')
def derivative_values_trace(list_of_terms, x_values, delta_x):
derivative_values = list(map(lambda x: derivative_of(list_of_terms, x, delta_x), x_values))
return trace_values(x_values, derivative_values, mode = 'line')
from graph import trace_values, plot
from graph import m_b_trace, plot, m_b_data, trace_values
from error import error_line_traces
angles = [.1, .2, .3, .4, .5, .6, .7]
predicted_distances = list(map(lambda angle: 40 * angle, angles))
data_trace = trace_values([.30, .50, .70], [8, 11, 17], name='actual shots')
observed_shot_angles = [.30, .50, .70]
observed_distances = [8, 11, 17]
def plot_data_and_model():
model_trace = trace_values(angles,
predicted_distances,
mode='lines',
name='model')
layout = {
'yaxis': {
'range': [0, 18],
'title': 'shot distance'
},
'xaxis': {
'title': 'shot angle'
}
}
return plot([data_trace, model_trace])
def plot_data_and_errors():
inputs = [.30, .40, .50, .60, .70]
predictions = list(map(lambda angle: 40 * angle, inputs))
def derivative_values_trace(list_of_terms, x_values, delta_x):
derivative_values = list(
map(lambda x: derivative_of(list_of_terms, x, delta_x), x_values))
return trace_values(x_values, derivative_values, mode='line')
from graph import model_trace, trace_rss, pair_colors, plot_side_by_side
from graph import trace_values
m_values = [.5, .6]
b = 100
rss_traces = []
model_traces = []
ad_spends = [800, 1500, 2000, 3500, 4000]
tshirt_sales = [330, 780, 1130, 1310, 1780]
actual_trace = trace_values(x_values=ad_spends, y_values=tshirt_sales)
for m in m_values:
rss_trace = trace_rss(m, b, ad_spends, tshirt_sales)
rss_traces.append(rss_trace)
built_model_trace = model_trace(m, b, ad_spends, tshirt_sales)
model_traces.append(built_model_trace)
pair_colors(model_traces, rss_traces)
figure = plot_side_by_side([actual_trace] + model_traces, rss_traces)
