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import pandas as pd
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from scipy import sparse
import pickle
def scripts_rework(path, character):
"""FOR GENERARTIVE MODEL TRAINING!!!
this functions split scripts for question, answer, context,
picks up the character, augments data for generative model training
and saves data in pickle format"""
df = pd.read_csv(path)
# split data for scenes
count = 0
df["scene_count"] = ""
for index, row in df.iterrows():
if index == 0:
df.iloc[index]["scene_count"] = count
elif row["person_scene"] == "Scene":
count += 1
df.iloc[index]["scene_count"] = count
else:
df.iloc[index]["scene_count"] = count
df = df.dropna().reset_index()
# rework scripts to filer by caracter utterances and related context
scripts = pd.DataFrame()
for index, row in df.iterrows():
if (row["person_scene"] == character) & (
df.iloc[index - 1]["person_scene"] != "Scene"
):
context = []
for i in reversed(range(2, 6)):
if (df.iloc[index - i]["person_scene"] != "Scene") & (index - i >= 0):
context.append(df.iloc[index - i]["dialogue"])
else:
break
for j in range(len(context)):
new_row = {
"answer": row["dialogue"],
"question": df.iloc[index - 1]["dialogue"],
"context": context[j:],
}
scripts = pd.concat([scripts, pd.DataFrame([new_row])])
new_row = {
"answer": row["dialogue"],
"question": df.iloc[index - 1]["dialogue"],
"context": [],
}
scripts = pd.concat([scripts, pd.DataFrame([new_row])])
elif (row["person_scene"] == character) & (
df.iloc[index - 1]["person_scene"] == "Scene"
):
context = []
new_row = {"answer": row["dialogue"], "question": "", "context": context}
scripts = pd.concat([scripts, pd.DataFrame([new_row])])
# load reworked data to pkl
scripts = scripts[scripts["question"] != ""]
scripts["context"] = scripts["context"].apply(lambda x: "".join(x))
scripts = scripts.reset_index(drop=True)
scripts.to_pickle("data/scripts_reworked.pkl")
# ===================================================
def scripts_rework_ranking(path, character):
"""FOR RAG RETRIEVAL !!!!
this functions split scripts for queation, answer, context,
picks up the cahracter and saves data in pickle format"""
df = pd.read_csv(path)
# split data for scenes
count = 0
df["scene_count"] = ""
for index, row in df.iterrows():
if index == 0:
df.iloc[index]["scene_count"] = count
elif row["person_scene"] == "Scene":
count += 1
df.iloc[index]["scene_count"] = count
else:
df.iloc[index]["scene_count"] = count
df = df.dropna().reset_index()
# rework scripts to filer by caracter utterances and related context
scripts = pd.DataFrame()
for index, row in df.iterrows():
if (row["person_scene"] == character) & (
df.iloc[index - 1]["person_scene"] != "Scene"
):
context = []
for i in reversed(range(2, 5)):
if (df.iloc[index - i]["person_scene"] != "Scene") & (index - i >= 0):
context.append(df.iloc[index - i]["dialogue"])
else:
break
new_row = {
"answer": row["dialogue"],
"question": df.iloc[index - 1]["dialogue"],
"context": context,
}
scripts = pd.concat([scripts, pd.DataFrame([new_row])])
elif (row["person_scene"] == character) & (
df.iloc[index - 1]["person_scene"] == "Scene"
):
context = []
new_row = {"answer": row["dialogue"], "question": "", "context": context}
scripts = pd.concat([scripts, pd.DataFrame([new_row])])
# load reworked data to pkl
scripts = scripts[scripts["question"] != ""]
scripts = scripts.reset_index(drop=True)
scripts.to_pickle("data/scripts.pkl")
# ===================================================
def encode(texts, model, contexts=None, do_norm=True):
"""function to encode texts for cosine similarity search"""
question_vectors = model.encode(texts)
if type(contexts) is list:
context_vectors = model.encode("".join(contexts))
else:
context_vectors = model.encode(contexts)
return np.concatenate(
[
np.asarray(context_vectors),
np.asarray(question_vectors),
],
axis=-1,
)
def encode_rag(texts, model, contexts=None, do_norm=True):
"""function to encode texts for cosine similarity search"""
question_vectors = model.encode(texts)
context_vectors = model.encode("".join(contexts))
return np.concatenate(
[
np.asarray(context_vectors),
np.asarray(question_vectors),
],
axis=-1,
)
# ===================================================
def encode_df_save(model):
"""FOR RAG RETRIEVAL DATABASE
this functions vectorizes reworked scripts and loads them to
pickle file to be used as retrieval base for ranking script"""
scripts_reopened = pd.read_pickle("data/scripts.pkl")
vect_data = []
for index, row in scripts_reopened.iterrows():
if type(row["context"]) is list:
vect = encode(
texts=row["question"],
model=model,
contexts="".join(row["context"]),
)
vect_data.append(vect)
else:
vect = encode(
texts=row["question"],
model=model,
contexts=row["context"],
)
vect_data.append(vect)
with open("data/scripts_vectors.pkl", "wb") as f:
pickle.dump(vect_data, f)
# ===================================================
def cosine_sim(answer_true_vectros, answer_generated_vectors) -> list:
"""FOR MODEL EVALUATION!!!!
returns list of tuples with similarity score"""
data_emb = sparse.csr_matrix(answer_true_vectros)
query_emb = sparse.csr_matrix(answer_generated_vectors)
similarity = cosine_similarity(query_emb, data_emb).flatten()
return similarity[0]
# ===================================================
def cosine_sim_rag(data_vectors, query_vectors) -> list:
"""FOR RAG RETRIEVAL RANKS!!!
returns list of tuples with similarity score and
script index in initial dataframe"""
data_emb = sparse.csr_matrix(data_vectors)
query_emb = sparse.csr_matrix(query_vectors)
similarity = cosine_similarity(query_emb, data_emb).flatten()
ind = np.argwhere(similarity)
match = sorted(zip(similarity, ind.tolist()), reverse=True)
return match
# ===================================================
def generate_response(
model,
tokenizer,
question,
context,
top_p,
temperature,
rag_answer="",
):
combined = (
"context:" + rag_answer +
"".join(context) + "</s>" +
"question: " + question
)
input_ids = tokenizer.encode(combined, return_tensors="pt")
sample_output = model.generate(
input_ids,
do_sample=True,
max_length=1000,
top_p=top_p,
temperature=temperature,
repetition_penalty=2.0,
top_k=50,
no_repeat_ngram_size=4,
# early_stopping=True,
# min_length=10,
)
out = tokenizer.decode(sample_output[0][1:], skip_special_tokens=True)
if "</s>" in out:
out = out[: out.find("</s>")].strip()
return out
# ===================================================
def top_candidates(score_lst_sorted, initial_data, top=1):
"""this functions receives results of the cousine similarity ranking and
returns top items' scores and their indices"""
scores = [item[0] for item in score_lst_sorted]
candidates_indexes = [item[1][0] for item in score_lst_sorted]
return scores[0:top], candidates_indexes[0:top]
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