rrobby86

# ESERCIZIO 1

for uid, iid in purchases_set:
    row = user_indices[uid]
    col = item_indices[iid]
    purchases[row, col] = 1


# ESERCIZIO 2

rrobby86

# ESERCIZIO 1

# 1a
with open("items.csv", "r") as f:
    items = {int(iid): name for iid, name in csv.reader(f, delimiter=";")}

# 1b
len(items)

# 1c

rrobby86

#!/bin/bash

# TODO:
# - start, stop, status commands
# - jupyterlab option
# - pass settings (e.g. profile) to aws cli

PROGNAME=$(basename $0)
AWS="aws"
OPEN="xdg-open"

rrobby86

# jq script to convert output of list-resource-record-sets into valid input for change-resource-record-sets
# NS and SOA records are removed
# based on https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/hosted-zones-migrating.html
# (aliases and domain name change are not handled)
.ResourceRecordSets|map(select(.Type!="NS" and .Type!="SOA")|{"Action":"CREATE","ResourceRecordSet":.})|{"Changes":.}

rrobby86

# ESERCIZIO 1

# 1a
model_a = LinearRegression()
model_a.fit(X_train, y_train)
print_eval(X_val, y_val, model_a)

pd.Series(model_a.coef_, index=X.columns)

# 1b

rrobby86

# ESERCIZIO 1

# 1a
lrm = LinearRegression()
summer_X_train = summer_train[["temp"]]
summer_y_train = summer_train["demand"]
lrm.fit(summer_X_train, summer_y_train)

# 1b
print_eval(summer_X_train, summer_y_train, lrm)

rrobby86

# ESERCIZIO 1

# 1a
data_summer.describe()

# 1b
data_summer["temp"].plot.hist(bins=20);
data_summer["demand"].plot.hist(bins=20);

# 1c

rrobby86

# ESERCIZIO 1

# 1a
tips.loc[0, "size"]

# 1b
tips.loc[tips["total_bill"].idxmax()]

# 1c
tips["tip"].mean()

rrobby86

# ESERCIZIO 1

# 1a
population[4]

# 1b
states[-3:]

# 1c
population[states == "Florida"]

rrobby86

# SOLUZIONE 1
for uid, iid in purchases_set:
    row = user_indices[uid]
    col = item_indices[iid]
    purchases[row, col] = 1


# SOLUZIONE 2a
# per ogni coppia ID, nome
for uid, name in users.items():