brockmanmatt

fig, axs = plt.subplots(4, 5, figsize=(15,10), sharex=True)
x = y = 0
test_length = 5

for issue in time_series:

  scores = pd.DataFrame(score_df)
  scores = scores[scores.pub == issue]

  scores[["n", "test", "train"]].plot(x="n", title="{}".format(issue), ax=axs[x,y], ylim=0)

brockmanmatt

test_length = 5

score_df = []

for issue in time_series:

  sma3 = time_series[[issue]].copy()
  dates = sma3.index
  for idx in range(len(time_series)-3):
    sma3.loc[dates[-idx], issue] = np.round((sma3.loc[dates[-idx-1], issue] + sma3.loc[dates[-idx-2], issue] + sma3.loc[dates[-idx-3], issue])/3)

brockmanmatt

fig, axs = plt.subplots(4, 5, figsize=(15,10), sharex=True)
x = y = 0
test_length = 5

for issue in time_series:
  sma3 = time_series[[issue]].copy() #set up lagged moving average 3 day
  dates = sma3.index
  for idx in range(len(time_series)-3):
    sma3.loc[dates[-idx], issue] = np.round((sma3.loc[dates[-idx-1], issue] + sma3.loc[dates[-idx-2], issue] + sma3.loc[dates[-idx-3], issue])/3)
  sma3.rename(columns={issue:"sma3"}, inplace=True)

brockmanmatt

from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import Pipeline

def get_naive_pca_model(df, issue, test_length, n=.9):
  print("doing {}".format(issue))

  myPCA = PCA(n)
  pca = Pipeline([

brockmanmatt

fig, axs = plt.subplots(4, 5, figsize=(20,12), sharex=True)
x = y = 0
test_length=5

for issue in time_series:

  myModel = pd.DataFrame(model_dict[issue]).sort_values(by="aic")[:1].reset_index()

  title="{} ({},{},{})\nMAPE: train {} test {}".format(issue, myModel.loc[0,"p"], myModel.loc[0, "d"], myModel.loc[0,"q"], myModel.loc[0, "train_mape"], myModel.loc[0, "test_mape"])

brockmanmatt

fig, axs = plt.subplots(4, 5, figsize=(20,12))
x = y = 0

for issue in time_series:

  pd.DataFrame(model_dict[issue]).plot(x="aic", y="test_mape", style=".", title="{}".format(issue), ax=axs[x,y])
  pd.DataFrame(model_dict[issue]).plot(x="aic", y="train_mape", style=".", title="{}".format(issue), ax=axs[x,y])

  if x == 0 and y==0:
    handles, labels = axs[0,0].get_legend_handles_labels()

brockmanmatt

model_dict = {}

for issue in time_series:

  model_dict[issue]=[]
  test_length = 5

  selected_series = time_series[[col for col in time_series.columns if (col.find(issue[issue.find("_"):]) > -1)]].shift()[1:].drop(columns=issue).add_suffix("_l1")
  pub_series = time_series[[col for col in time_series.columns if (col.find(issue[:issue.find("_")]) > -1)]].drop(columns=issue).shift()[1:].add_suffix("_l1")
  selected_series = selected_series.join(pub_series).join(time_series[issue])

brockmanmatt

fig, axs = plt.subplots(1,3, figsize=(12,8))

issue = time_series.columns[0]
test_length = 10
selected_series = time_series[[col for col in time_series.columns if (col.find(issue[issue.find("_"):]) > -1)]].shift()[1:].drop(columns=issue).add_suffix("_l1")
pub_series = time_series[[col for col in time_series.columns if (col.find(issue[:issue.find("_")]) > -1)]].drop(columns=issue).shift()[1:].add_suffix("_l1")
selected_series = selected_series.join(pub_series).join(time_series[issue])
x = 0

for p in [1,5,10]:

brockmanmatt

from statsmodels.tsa.statespace.sarimax import SARIMAX

def gen_SARIMA_result(p,d,q, df, issue, test_length): #takes pdq, data, issue to use

  s_model = SARIMAX(endog = df[issue][:-test_length],
                    exog = df[[x for x in df.columns if x != issue]][:-test_length],
                    order=(p,d,q), seasonal_order=(1,0,1,7)).fit()

  f_ru = df[[issue]].copy()[1:] #haven't bothered to change this, but it's the results

brockmanmatt

mySources = ["cnn.com", "washingtonpost.com", "nytimes.com", "foxnews.com"]

#unnest the entries with multiple sources in them
df = df.set_index(df.columns.drop('SOURCES',1).tolist()).SOURCES.str.split(';', expand=True).stack().reset_index().rename(columns={0:'SOURCES'}).loc[:, df.columns]

df.DATE = df.DATE.apply(lambda x: str(x)) #convert date
df.DATE = pd.to_datetime(df.DATE)
df.fillna("", inplace=True)
df.set_index("DATE", drop=True, inplace=True)